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Controversy and Group Certainty Jointly Shape Everyday Moral Judgments

Ziyu Chen, Minjeong Shin, Tuan Dung Nguyen, Colin Klein, Chenhao Tan, Nick Schuster, Nicholas George Carroll, Alasdair Tran, Lexing Xie

arXiv:2609.04750v1cs.CY

TL;DR

People’s responses to disagreement and expressed certainty in naturalistic moral discussions remain understudied. This paper tests these signals in a randomized experiment and finds that all three social-information treatments increased both weakening and strengthening of judgments relative to control.

  • Problem

    Evidence remains limited on how people respond to disagreement and expressed certainty drawn from naturalistic moral discussion rather than explicit norms or stylized social cues.

    Source: §A.1.4 · p. 31

  • Method

    The authors randomly assigned participants to see controversy information, group-certainty information, both, or an active control, then measured changes in verdicts and confidence.

    Source: Main · p. 2

  • Results

    All three treatments increased both weakening and strengthening versus Control; Controversy raised weakening from 5.3% to 15.9%.

    Source: Overall treatment effect · p. 6

  • Takeaways & Limitations

    For platforms and AI systems summarizing human judgments, representing both division and side-specific certainty can give a fuller account of collective opinion without treating either as moral correctness.

    Source: Discussion · p. 12

    The findings do not establish that judgments changed by these signals are more accurate or better justified.

    Source: Discussion · p. 12

Abstract

from arXiv · show

Everyday moral life rarely resembles a trolley problem. It involves disputes about families, relationships, work, money, and care, situations in which people often encounter the judgments of others. We examined how judgments about nuanced interpersonal dilemmas respond to social information that conveys collective opinion without revealing the arguments behind it. Specifically, we studied two signals: controversy, the extent to which community judgments are divided between two opposing verdicts; and group certainty, the confidence expressed by each side. We derived these signals from 54,827 judgments on 135 dilemmas posted to Reddit's r/AmItheAsshole and presented them separately or together in a preregistered randomized experiment (N = 2,159). Relative to the control condition, all three treatments increased both weakening, a changed verdict or reduced confidence, and strengthening, increased confidence without a verdict change. Thus, aggregated social information can reshape moral judgments even without arguments. Controversy alone tripled the weakening rate, from 5.3% to 15.9%. Shown alone, certainty on either side was associated with movement toward that side. Shown alongside controversy, only like-minded certainty remained clearly associated with less weakening. This asymmetry of influence is double-edged: the confidence of like-minded others may help minorities resist majority pressure, but it may also insulate mistaken judgments from correction.

Main

Argument-free summaries of collective controversy and side-specific certainty shifted judgments of everyday interpersonal dilemmas, with associations that depended on whether the signals appeared separately or together.

Main

  • The preregistered randomized experiment tested 2,159 participants on 135 dilemmas drawn from 54,827 community judgments.Participants received controversy, certainty, both signals, or an active Control task; each evaluated two dilemmas, yielding 4,318 judgment observations.Source: Main · p. 2
  • All three social-information conditions increased both weakening and strengthening relative to Control across diverse interpersonal dilemmas.The conditions were Controversy, Certainty, and Controversy+Certainty.Source: Main · pp. 2–3
  • When signals appeared separately, greater disagreement was associated with more weakening and less strengthening, while in-group and out-group certainty showed opposite patterns.Higher in-group certainty was associated with less weakening and more strengthening; higher out-group certainty showed the reverse. In-group means that community judgments agreed with a participant’s initial verdict.Source: Main · p. 3
  • When Controversy and Certainty appeared together, disagreement associations remained, but only higher in-group certainty remained associated with less weakening.Minority judgment holders weakened less often when their own side was equally or more certain than the majority.Source: Main · p. 3
  • Participants’ written explanations mentioned controversy most often, and in-group certainty more often than out-group certainty.This pattern corresponded to the asymmetric certainty associations when controversy and certainty appeared together.Source: Main · p. 3

Experimental setup

The study used real-life AITA dilemmas to test how participants’ verdicts and confidence respond to aggregated social signals. Its design measured changes jointly and defined those signals relative to each participant’s initial verdict.

Verdicts and measures

  • AITA labels were collapsed into YA (YTA/ESH) and NA (NTA/NAH), while majority and minority were defined by each dilemma’s Reddit verdict counts.Participant judgments comprised their verdict and confidence before and after treatment; treatment measures were coded relative to the participant’s initial verdict, not as YA versus NA predictors.Source: Experimental setup · pp. 3, 5

Dataset

  • The MORALMOMENTS dataset contains 135 AITA dilemmas across 32 topics, each with at least 50 verdicts, totaling 54,827.Posts were selected to cover everyday topics and controversy levels, with highly controversial posts oversampled; an LLM classified comment certainty within each verdict group.Source: Experimental setup · p. 3, Figure 1 · p. 4

Dataset profile

  • At low controversy, the majority was usually more certain, but near an even verdict split, minority certainty could be either higher or lower.Controversy is the minority share of verdicts, from 0 for full consensus to 0.5 for an even split; certainty difference is majority-group certainty minus minority-group certainty.Source: Experimental setup · pp. 3, 5, Figure 1 · p. 4

Study design

  • The randomized three-step task collected an initial verdict and confidence, presented assigned social information or a control task, then collected verdict and confidence again.Participants could optionally explain their response; the control group completed a topic-selection task instead of seeing others’ judgments.Source: Figure 1 · p. 4, Experimental setup · p. 5

Treatment conditions

  • Participants saw Controversy, Certainty, both signals, or control; the signals were displayed as percentages with brief descriptions and infographics.Controversy showed the fraction of AITA users agreeing or disagreeing with the participant’s initial verdict; Certainty showed certain and uncertain fractions separately for each side.Source: Figure 1 · p. 4, Experimental setup · p. 5

Measures

  • Judgment change was modeled jointly: weakened meant a changed verdict or lower confidence, strengthened meant unchanged verdict with higher confidence, and maintained meant both stayed the same.The analysis treated verdict and confidence as one reconsideration process rather than separate outcomes.Source: Experimental setup · p. 5

Results

All three aggregate social-information treatments increased both judgment weakening and strengthening relative to Control. The associations differed by signal and social position, with controversy linked to more weakening and like-minded certainty sometimes linked to less weakening or more strengthening.

Overall treatment effect

  • Weakening rose from 5.3% in Control to 10.6% under Certainty, 15.9% under Controversy, and 12.1% under Controversy+Certainty; strengthening also rose in each treatment.Among observations eligible for strengthening, rates rose from 8.6% in Control to 25.6% under Certainty, 25.9% under Controversy, and 20.9% under Controversy+Certainty. Weakening and strengthening use different denominators and should not be compared directly.Source: Overall treatment effect · pp. 6–7, Figure 2 · p. 7

When do judgments weaken or strengthen?

  • Higher initial confidence predicted 46% lower odds of weakening; when shown alone, in-group certainty was associated with less weakening and more strengthening, while out-group certainty showed the opposite pattern.Greater disagreement was associated with more weakening and less strengthening. With both signals displayed, disagreement remained associated with both outcomes, but only in-group certainty retained a clear association with less weakening; out-group certainty showed no clear independent association with either outcome.Source: When do judgments weaken or strengthen? · pp. 7, 9
  • Minority participants weakened more than majority participants under Controversy alone (27.3% versus 7.9%), while relative certainty differentiated weakening among majority but not minority participants when both signals appeared.In the combined condition, majority weakening was 4.0% when in-group certainty was equal or greater versus 11.7% when it was lower; the minority difference (18.1% versus 20.3%) was not statistically significant. Predicted weakening increased with disagreement, especially for minority participants, but was lower with combined signals and when the in-group was at least as certain as the out-group.Source: When do judgments weaken or strengthen? · pp. 7, 9, Figure 3 · p. 8
  • Strengthening was more common among majority participants and when in-group certainty was at least as high as out-group certainty (32.4% versus 16.6% under Certainty).Disagreement was associated with less strengthening; certainty alone showed the reverse of its weakening associations, but neither certainty coefficient independently predicted strengthening when both signals appeared. Combined treatment did not increase strengthening beyond Controversy alone; higher intellectual humility was associated with both weakening and strengthening.Source: Figure 3 · p. 8, When do judgments weaken or strengthen? · p. 9

Exploratory analyses

  • Controversy was the most-mentioned signal in optional explanations, and 60–81% of explanations mentioning a displayed signal accepted it as relevant.Controversy was mentioned in 32% of explanations when shown alone and 29% with Certainty; in-group certainty was mentioned more often than out-group certainty in both certainty conditions. Acceptance of controversy was more often associated with weakening, while acceptance of in-group certainty was more often associated with strengthening; these optional, observational explanations are exploratory, not direct evidence of reasoning.Source: Exploratory analyses · p. 10, Figure 4 · p. 10
  • Exploratory dilemma analyses found no consistent relationship between weakening or strengthening and scenario severity, affected party, or vulnerable-person involvement.Two cautious pairwise differences were greater weakening for family than friend dilemmas and less strengthening when a child was involved. Of participants giving meaningful optional feedback, 68.6% described a positive experience, 20.3% mentioned learning or reflection, 5.2% criticized the design, and 2.4% described a negative experience; these percentages may not represent the full sample.Source: Exploratory analyses · pp. 10–11

Discussion

Moral judgments respond to aggregate controversy and group certainty, but the study establishes that these signals prompt updating—not that revisions become more accurate or better justified.

Discussion

  • All social-information treatments increased weakening and strengthening; disagreement showed the strongest association with updating, while only like-minded certainty remained associated with less weakening when controversy was visible.When certainty appeared alone, higher in-group certainty was associated with less weakening and more strengthening, while higher out-group certainty showed the reverse pattern.Source: Discussion · p. 11
  • The study extends research on aggregate opinion and social confidence to varied interpersonal moral dilemmas, and shows that certainty’s association with updating differs when controversy is visible.The findings bridge work on aggregate opinion in factual tasks (Oktar, Lombrozo, & Griffiths, 2024), confidence in small groups (Bang et al., 2017; Moussa¨ıd, K¨ammer, Analytis, & Neth, 2013), and social influence in moral judgment.Source: Discussion · p. 11
  • The study shows that social signals prompt moral-judgment updating, but does not establish whether revised judgments are more accurate or better justified.The dilemmas generally lack independently verifiable answers, and the summaries supplied no new arguments or first-order evidence; like-minded confidence could preserve well-founded minority dissent or shield mistaken judgments from correction.Source: Discussion · pp. 11–12
  • The mechanism behind asymmetric certainty effects remains unresolved because participants’ retrospective, self-selected explanations could not distinguish among possible reasoning processes.Future work could directly measure attention and information processing, manipulate candidate mechanisms, or elicit reasoning before and after exposure.Source: Discussion · p. 12
  • The design establishes immediate responses to controlled summaries in this sample, not generalization across populations and platforms or persistence after repeated exposure.The study used English-language Reddit dilemmas, a predominantly English-speaking sample, and concise summaries; it did not compare the summaries with substantive reasons or arguments.Source: Discussion · p. 12
  • Because the outcomes measure revision direction rather than improvement, future studies should test whether displays broaden consideration of affected parties or alter accuracy on verifiable factual tasks.The paper proposes longitudinal designs to test whether judgment change persists.Source: Discussion · p. 12
  • Collective opinion includes both how divided a group is and which side appears more certain, offering platforms and AI systems a fuller summary without implying moral correctness.Neither popularity nor certainty should be treated as a marker of moral correctness.Source: Discussion · p. 12

Methods

The methods combine a Reddit-derived dataset of interpersonal dilemmas and group social signals with a four-condition randomized survey, then define participant-level outcomes and analyze judgment changes and optional text responses. The study also evaluates model specifications and annotates dilemma consequences.

MORALMOMENTS dataset and social-signal construction

  • MORALMOMENTS contains 135 dilemmas across 32 topics and 54,827 community verdicts, sampled from AITA posts to span controversy and oversample closely divided verdicts.Controversy was defined as the minority share of YA or NA verdicts; group certainty was the proportion of comments classified as certain within each verdict group, using text rather than Reddit votes.Source: MORALMOMENTS dataset and social-signal… · p. 13

Participants, recruitment, and exclusions

  • The final analytic sample comprised 2,159 adults recruited through Prolific, who passed at least two of three attention checks and contributed 4,318 participant–dilemma observations.Participants were assigned to one of four conditions and each completed two randomly selected dilemmas; 2,159 was the final sample from 2,171 completed submissions.Source: Participants, recruitment, and… · p. 13

Experimental design

  • The four-arm randomized design compared controversy, certainty, both signals, and a no-social-information control across initial judgment, assigned display or topic task, and final judgment.Participants recorded a verdict and confidence before and after the information or control task; participant confidence was distinct from certainty inferred from Reddit commenters’ text.Source: Experimental design · p. 14

Outcome measures and analysis variables

  • Judgment responses were coded as weakened, maintained, or strengthened relative to each participant’s initial verdict–confidence pair, with strengthening analyzed only below the confidence ceiling.Reversals counted as weakening regardless of final confidence; weakening regressions included all 4,318 observations, while strengthening regressions used n = 1,924.Source: Outcome measures and analysis variables · pp. 15–16

Statistical analysis and regression

  • The analysis compared binary outcome rates with Barnard’s exact test and fitted separate logistic regressions; model ablation used 10-fold cross-validation and held-out mean log-likelihood.Ablation compared model structures, covariate blocks, and continuous versus binary social-signal representations; covariate blocks were retained only when they improved held-out mean log-likelihood.Source: Statistical analysis and regression · pp. 16–18
  • The selected weakening and strengthening models were GLMs without dilemma factors, using continuous treatment information; covariates retained differed by outcome.The weakening model used all 4,318 observations and retained GDMS, intellectual humility, sex, and nationality; the strengthening model used n = 1,924 and retained GDMS and intellectual humility. Neither retained dilemma topic or participant- or dilemma-level effects.Source: Statistical analysis and regression · p. 18

Analysis of written explanations

  • Among 4,318 observations, 2,769 included optional written explanations; screening left 2,744 explanations from 1,528 participants, classified for reasoning and references to social signals.Reasoning classification achieved macro F1 = 0.69; treatment-reference coding had detection accuracy of 0.91–0.95 and three-class macro F1 of 0.75–0.83. Feedback analysis retained 458 meaningful entries and coded them into eight categories.Source: Analysis of written explanations · p. 18, Analysis of participant feedback · pp. 18–19

Analysis of dilemma consequences

  • The 135 dilemmas were annotated for severity, affected parties, and vulnerability, with validation F1 scores of 0.71–0.92 across tasks.Severity was averaged across three annotations and categorical labels were determined by majority vote; the annotations supported descriptions of consequences and exploratory analyses of their associations with weakening and strengthening.Source: Analysis of dilemma consequences · p. 19

Extended figures

The extended tables document logistic-regression models of judgment weakening and strengthening, and categorize participants’ written explanations by social signal, condition, and outcome.

Extended figures

  • Tables 2–3 report logistic-regression coefficient and odds-ratio estimates with 95% confidence intervals for judgment weakening and strengthening, respectively.The weakening model excludes dilemma as a factor and uses cross-validated covariate selection; the strengthening model includes GDMS and IH as covariates.Source: Table 2 · p. 20, Table 3 · p. 21
  • Table 4 categorizes selected written explanations by mentions of disagreement rate or in-group or out-group certainty, further grouping them by condition and outcome.Source: Table 4 · p. 22

Competing Interests. … B.3 Extracting controversy and certainty

This section motivates examining how aggregate controversy and group-expressed certainty shape responses to naturalistic moral disputes, distinct from argument-based persuasion or claims about correct verdicts. It describes aggregating AITA verdicts and discussion features, selecting 135 dilemmas for MORALMOMENTS, and calculating controversy from verdict shares.

A.1.1 Everyday moral dilemmas and online moral norms

  • Everyday interpersonal dilemmas offer narrative detail, interpersonal stakes, and overlapping concerns that theoretical dilemmas and standardized vignettes do not capture.Prior AITA work finds that dilemmas often combine multiple topics and that many everyday norms lack strict consensus.Source: §A.1.1 · p. 30

A.1.3 Moral conviction and moralization

  • The study measures verdicts and stated confidence before and after social information, not moral conviction, moral foundations, or moralization.Its measures should not be interpreted as direct measures of an underlying conviction’s strength or moral status.Source: §A.1.3 · p. 31

A.1.4 Social influence in moral judgment

  • Prior moral-influence studies use stylized scenarios, explicit norms, or direct social cues, leaving open how people respond to naturalistic disagreement and expressed certainty.Because everyday moral disputes lack independently verifiable answers, observing others’ verdicts may prompt revision but cannot establish which verdict is correct or better justified.Source: §A.1.4 · p. 31

A.2 Social influence on individual judgment

  • Unlike argument-based communication and direct group interaction, the experiment examines responses to anonymous aggregate signals about others’ views.Prior research on communication studies arguments, narratives, and repeated engagement; work on group influence leaves the interpretation of anonymous aggregate signals distinct.Source: §A.2.1 · p. 32, §A.2.2 · p. 33

A.2.3 Aggregate social evidence

  • Earlier aggregate-opinion studies generally omit each side’s expressed certainty, while confidence research mainly examines dyads; the interaction of controversy and group certainty remains untested.The cited prior work also lacks a certainty-only condition or a direct test of whether controversy changes certainty’s influence.Source: §A.2.3 · p. 33
  • The study extends aggregate social-evidence research to naturalistic moral dilemmas, independently varying controversy and certainty and distinguishing certainty on like-minded and opposing verdict sides.This design tests whether visible disagreement changes how certainty affects responses.Source: §A.2.3 · p. 33

A.3 Certainty as social information

  • Here, group certainty means how confidently people on each verdict side appear to hold their collective view, not whether that view is objectively correct.The study aggregates linguistic indications of certainty by verdict group to examine responses to certainty from people who agree or disagree with a participant’s initial judgment.Source: §A.3 · p. 33, §A.3.3 · p. 34

B.1 The r/AmItheAsshole community

  • The study draws on AITA, a Reddit community where users post interpersonal conflicts and receive community verdicts; its source dataset contains 102,998 posts and over 7 million comments.From 2014 to 2020, annual posts grew from 234 to 134,915, while average comments per thread rose from 10.7 to 79.9.Source: §B.1 · p. 35

B.1.1 The structure of AITA

  • Top-level AITA verdicts were grouped into YA (YTA/ESH) and NA (NTA/NAH), while INFO was removed; post records aggregate topic, verdict volume, controversy, and certainty.Source: §B.1.1 · pp. 36–37

B.2 The MORALMOMENTS dataset

  • MORALMOMENTS contains 135 dilemmas, 54,827 verdicts, 32 topics, and controversy levels spanning 0–0.5.Selection prioritized substantial discussion (at least 50 top-level YA/NA verdicts), topic diversity, and controversy; 105 posts were sampled at 0.3–0.5 controversy and 30 at 0.0–0.3.Source: §B.2 · pp. 37–38

B.3 Extracting controversy and certainty

  • Controversy was the minority share of top-level YA/NA verdicts, ranging from 0 for consensus to 0.5 for an even split.Source: §B.3 · p. 39

B.3.1 Individual certainty … C.2.1.1 Model-selection details

This section describes how individual certainty and verdict controversy were operationalized and used in the survey experiment, including its measures, conditions, and dilemma stories. It also outlines the sample, covariates, and statistical procedures for analyzing changes in judgments.

B.3.1 Individual certainty

  • Across 200 comments, all human annotators agreed at least in pairs, and overall Krippendorff’s α was 0.69; Annotator A reached ρ = 0.87 and α = 0.85.A 4-point Likert scale was used because a preliminary 5-point scale produced lower inter-annotator agreement (α = 0.58).Source: §B.3.1 · pp. 39–40, Table 6 · p. 40
  • Few-shot prompting with CoT and GPT-3.5 Turbo was selected for large-scale annotation, achieving ρ = 0.78 and α = 0.78.The method was applied to 54,827 comments across 135 posts, using temperature 0.2 and top p 0.1.Source: §B.3.1 · p. 41, Table 7 · p. 42

B.3.2 Group controversy and certainty

  • For binary certainty classification, few-shot CoT with GPT-3.5 Turbo achieved the best test set F1 score of 0.92 and AUC of 0.88.For group certainty, levels 1–3 were grouped as “not-very-certain” and level 4 as “very-certain”; majority groups generally showed higher certainty, though minority certainty sometimes exceeded it.Source: Table 7 · p. 42, §B.3.2 · pp. 43–45, Figure 9 · p. 45

B.4 Survey experiment protocol

  • The survey tested whether controversy and group certainty, presented separately or together, changed judgments of real-life moral dilemmas.Source: §B.4 · p. 46, Figure 10 · p. 46

Ethical approval declarations:

  • The research received Australian National University Human Research Ethics Committee approval under Protocol H/2023/1292, and participants provided informed consent.Source: Ethical approval declarations · p. 46

Recruitment and payment

  • Prolific recruited fluent English-speaking adults aged 18 or older who had not taken an earlier study version; analysis retained participants passing at least two of three attention checks.Participants received £1.95, and median completion time was 14 minutes (861.5 seconds).Source: Recruitment and payment · p. 47

Phase I: Project description, Consent and Training

Phase II: Treatment and story randomization

  • Participants were randomized to Controversy, Certainty, Controversy+Certainty, or Control, and to two of 135 dilemmas, before recording initial and final verdicts and confidence.The control task provided no social information; treatment signals were conveyed through percentages, short descriptions, and infographics.Source: Figure 10 · p. 46, Phase II: Treatment and story… · p. 47

Phase III: Background Survey, Feedback, and Conclusion

  • After the dilemmas, participants completed decision-style, personality, and intellectual-humility questionnaires, then optional feedback and debriefing before Prolific payment.The decision-style questionnaire was a shortened 20-item GDMS; the other measures were the Ten-Item Personality Inventory and six-item Intellectual Humility Scale.Source: Phase III: Background Survey,… · p. 47

B.4.2 Reducing survey items in GDMS

  • The item-reduction analysis sought four-item subsets that retained variance, initially using 166 participants and then validating reduced scales with 80 follow-up participants.The follow-up participants completed the four-item survey at least one month after the original five-item survey.Source: §B.4.2 · p. 51

Variance Retention

Pearson Correlation

  • Four-item versions correlated r = 0.97–0.99 with the original five-item scales at baseline, compared with r = 0.92–0.96 for three-item versions.The passage reports that both sets of correlations were statistically significant; Table 8 covers the 166 participants who completed the original survey.Source: Pearson Correlation · p. 53, Table 8 · p. 53

Test-Retest Reliability in the Follow-Up Study

  • Among 80 follow-up participants, four-item versions showed temporal stability: same-form follow-up-versus-baseline correlations ranged from r = 0.56 to r = 0.73, all with p-values below 1.1 × 10−14.Compared with original five-item baseline scores, four-item follow-up scores correlated r = 0.56 to r = 0.73, with p-values below 1.6 × 10−14.Source: Test-Retest Reliability in the… · p. 53, Table 9 · p. 54

Different Implications for Measurement Quality (Variance Retention vs Correlation)

  • For the Intuitive factor, the three-item form retained 70% of raw item variance while its composite score correlated ρ ≈0.96 with the five-item form.The passage explains that discarded items’ unique variance lowers variance retention, while shared latent-trait variance preserves respondent ranking.Source: Different Implications for Measurement… · p. 55

Survey Reduction Decision

  • Three-item GDMS scales were selected to reduce participant burden by 40% while retaining high baseline correlations and stable rank ordering over time.They retained about 70% of total variance and correlated at ρ ≥0.95 with original five-item scores at baseline; four-item scales retained approximately 90% of variance but added little rank-order stability or temporal consistency.Source: Survey Reduction Decision · p. 55

B.4.3 Definition of treatment variables and covariates

  • Regression covariates included treatment measures, dilemma and topic identifiers, initial confidence, personality and decision-style measures, intellectual humility, and demographics.Dilemma and topic identifiers were represented by 135 and 32 dummy variables, respectively; demographic controls were country, age, and gender.Source: §B.4.3 · pp. 55, 57

B.5.2 Participants’ profile

  • After attention-check filtering, the final analytic sample comprised 2,159 participants, with a mean age of 38.7 and near balance by sex.Most participants lived in the United States (39.1%), South Africa (23.6%), or the United Kingdom (11.9%); mean intellectual humility was 0.71 on a 0–1 scale.Source: §B.5 · p. 58, §B.5.2 · p. 60

B.5.1 Time distribution and comment engagement

  • Median survey duration was 14.28 minutes in Control and 14.14–14.76 minutes across treatments, with substantially overlapping timing ranges.Among the fastest 5% by section, written explanations or feedback were still provided by 57.4% for training, 33.3% for the main survey, and 55.6% for the background section.Source: Table 13 · p. 58, §B.5.1 · pp. 58, 60, Table 15 · p. 60

C.1.1 Verdict and confidence change rate

  • Verdict change, confidence decreases among those keeping their verdict, and confidence increases all rose in each treatment relative to control.Verdict-change rates were 3.2% in control versus 6.6% with Certainty, 8.9% with Controversy, and 5.2% with Controversy+Certainty; confidence-decrease rates were 2.2% versus 4.3%, 7.7%, and 7.3%; confidence-increase rates were 3.9% versus 11.6%, 12.9%, and 10.4%. Every treatment comparison was significant.Source: Table 19 · p. 62, §C.1.1 · pp. 62–63

C.1 Overall judgment changes

  • Among participants who changed their verdict, treatment conditions did not significantly alter confidence change relative to control.Rate comparisons used two-sided Barnard exact tests with Benjamini–Hochberg correction within families of related comparisons.Source: §C.1 · p. 62, Table 20 · p. 63, §C.1.1 · p. 63

C.1.2 Weakening and strengthening rate

  • Weakening rose from 5.3% under control to 10.6% with Certainty, 15.9% with Controversy, and 12.1% with Controversy+Certainty; all comparisons were significant.Controversy produced the most weakening, while the combined condition produced less weakening than Controversy alone. Among participants with initial confidence below the ceiling (c < 4), strengthening rose from 8.6% under control to 25.6%, 25.9%, and 20.9%, respectively; all comparisons were significant.Source: Table 21 · p. 63, §C.1.2 · p. 63, Table 22 · p. 64

C.2.1 Weakening regression details

  • The weakening model treated verdict reversal or lower confidence without reversal as a weakened response across all 4,318 participant–dilemma observations.Coefficients were estimated separately from those in the strengthening model.Source: §C.2.1 · p. 65

C.2.1.1 Model-selection details

  • Model selection used mean held-out log-likelihood, preferring higher, less negative scores because the metric rewards calibrated probability estimates rather than threshold-based accuracy.It penalizes false confidence and missed events symmetrically and is a strictly proper scoring rule.Source: §C.2.1.1 · p. 65
  • Ten-fold cross-validation selected covariate blocks through single-block comparisons and greedy forward additions, then refit the best-scoring step on full data.Only grouped nationality or country categories with n > 100 were retained for later steps; nationality outperformed country in all four specifications, while raw categories reduced out-of-sample fit. Blocks not useful alone were retested with others, including jointly as a final step.Source: §C.2.1.1 · pp. 65–66

C.2.1.2 Ablation results · C.2.1.3 Best-model coefficients · C.2.2 Treatment and social position

The best weakening model retained GDMS, IH, Sex, and Nationality, while treatment effects and social position shaped whether judgments weakened. Controversy and certainty showed different associations with weakening across treatment conditions and social-position groups.

C.2.1.2 Ablation results

  • GDMS was the strongest single-block predictor in all four specifications (∆ℓ̄ ≈ 0.007), while Topic and raw country or nationality variables reduced out-of-sample fit.Topic had ∆ℓ̄ ≈ −0.011; raw country and nationality variables had ∆ℓ̄ from −0.034 to −0.093, and a participant-level random intercept in glmmTMB had ∆ℓ̄ = −0.462.Source: §C.2.1.2 · p. 66, Table 23 · p. 66
  • GDMS entered first in every forward-selection specification; the best GLM without dilemma factors reached step 4, while the GLM with dilemma factors reached its best at step 3.The GLM without dilemma factors selected GDMS, IH, Sex, and Nationality; the GLM with dilemma factors selected GDMS, Nationality, and IH, and the final addition of Topic, Personality, Age, and a participant random intercept sharply reduced fit (∆ℓ̄ = −0.300).Source: §C.2.1.2 · pp. 66–67, Table 24 · p. 67
  • The best model was the GLM without dilemma as a factor at step 4 (GDMS + IH + Sex + Nationality; ℓ̄ = −0.3062, SE = 0.0091), nearly matching glmmTMB (ℓ̄ = −0.3063, SE = 0.0092).The glmmTMB dilemma-level random-effect variance was 4.77 × 10^-9, and its coefficient estimates matched the GLM without dilemma as a factor to at least three decimal places.Source: §C.2.1.2 · p. 67, Table 24 · p. 67

C.2.1.3 Best-model coefficients

  • In the best weakening model, disagreement rate increased weakening in Controversy (β = 4.52, p < 0.001) and Controversy+Certainty (β = 3.74, p < 0.001), while in-group certainty reduced it in both conditions.In the Certainty condition, in-group certainty reduced weakening (β = −2.46, p < 0.001), whereas out-group certainty increased it (β = 2.06, p < 0.001); initial confidence was negatively associated with weakening (β = −0.21, p = 0.003).Source: §C.2.1.3 · pp. 67–68
  • Dependent and Spontaneous decision-making styles were the strongest individual predictors of weakening (β = 0.29, p < 0.001; β = 0.30, p = 0.002), and IH had a positive effect (β = 0.19, p = 0.012).Relative to female participants, male participants had higher weakening odds (β = 0.253, SE = 0.106, z = 2.38, p = 0.017, OR = 1.29, 95% CI [1.05, 1.59]).Source: §C.2.1.3 · p. 68
  • South Africa had higher weakening than Australia (β = 0.80, p = 0.009), the Other group (β = 0.48, p = 0.004), and the United Kingdom at trend level (β = 0.40, p = 0.055).The United States also exceeded Australia (β = 0.58, p = 0.046) and trended above the Other group (β = 0.27, p = 0.086); no contrasts among Canada, the United Kingdom, the United States, or Other were significant.Source: §C.2.1.3 · p. 68, Table 25 · p. 68

C.2.2 Treatment and social position

  • Under Controversy, minority judgments weakened more than majority judgments (27.3% vs 7.9%); under Controversy+Certainty, the gap persisted in both certainty cells (18.1% vs 4.0%; 20.3% vs 11.7%).Under Certainty, weakening was higher when the in-group was less certain than the out-group (18.5% vs 5.3%); in the combined condition, the certainty contrast was significant only within the majority position (11.7% vs 4.0%).Source: §C.2.2 · p. 68, Table 26 · p. 69
  • Controversy alone produced more weakening than Controversy+Certainty in three subcells, while adding certainty to controversy lowered weakening in the cells that differed.The significant contrasts included 7.9% vs 4.0%, 27.3% vs 18.1%, and 18.1% vs 5.3%; the 27.3% vs 20.3% contrast fell short after correction (pBH = .063).Source: §C.2.2 · pp. 68–69, Table 27 · p. 69

C.2.3 Regression robustness: continuous treatment variables versus binary social-position variables · C.3 When do judgments strengthen? · C.3.1 Strengthening regression: model selection and estimates

Continuous treatment-information variables improved weakening prediction more than binary social-position variables. The strengthening analysis examined confidence increases among participants who had room to become more confident.

C.2.3 Regression robustness: continuous treatment variables versus binary social-position variables

  • The binary variables represented whether a participant was in the minority and whether in-group certainty matched or exceeded out-group certainty.Minority status meant more than 50% of AITA verdicts opposed the participant’s initial verdict; lower in-group certainty was coded as In < Out.Source: §C.2.3 · p. 69
  • Both representations were tested separately against the same base model using 10-fold cross-validation and held-out mean log-likelihood.The base model included initial confidence and condition indicators; the continuous and binary blocks were mutually exclusive alternatives.Source: §C.2.3 · p. 69
  • The continuous block preserved variation in displayed percentages, whereas the binary variables captured broad social positions used in descriptive comparisons.The continuous treatment-information block was retained after producing the larger predictive gain.Source: §C.2.3 · p. 70
  • The continuous block gained 0.0227 held-out mean log-likelihood versus 0.0182 for binary, a +0.0045 advantage, and was retained.Continuing greedy ablation with the continuous block reproduced the primary covariate selection and final held-out score in Table 24 (¯ℓ= −0.3062).Source: §C.2.3 · pp. 69–70, Table 28 · p. 70

C.3 When do judgments strengthen?

  • The strengthening analysis covered model selection, coefficient estimates, social-position comparisons, and continuous-versus-binary robustness, using observations with initial confidence below 4.Confidence could not increase at the ceiling, so the analysis was restricted to c < 4.Source: §C.3.1 · p. 70, §C.3 · p. 70

C.3.1 Strengthening regression: model selection and estimates

  • Among 1,924 strengthening observations, 391 (20.3%) showed higher confidence while retaining the same verdict.The logistic strengthening outcome was estimated separately from the weakening model.Source: §C.3.1 · p. 70

C.3.1.1 Ablation results … 2) Best Model - Full Coefficient Table

Across the reported models, disagreement and certainty information were associated with verdict changes and confidence increases in different ways, while GDMS and intellectual humility were the most consistent predictors across outcomes. Model selection and robustness checks favored compact predictor sets and continuous treatment information.

C.3.1.1 Ablation results

  • GDMS and intellectual humility were the only blocks selected across all strengthening specifications, with GDMS entering first and no further block improving fit.The GLM, feglm, and glmmTMB best models were reached after two steps; their mean log-likelihoods were −0.4567, −0.5059, and −0.4569, respectively. Topic produced the largest negative single-block gains in GLM and glmmTMB.Source: §C.3.1.1 · p. 70, Table 30 · p. 71

C.3.1.2 Best-model coefficients

  • In the best strengthening model, disagreement reduced strengthening (β = −4.08), while in-group certainty increased it and out-group certainty reduced it.In the certainty condition, the respective odds ratios were 6.17 and 0.30; in the combined condition, disagreement also reduced strengthening (β = −3.45). Intuitive and avoidant styles were positive predictors, dependent style was negative, and intellectual humility was positive across both outcomes.Source: §C.3.1.2 · pp. 71–72

C.3.2 Strengthening by social position

  • Strengthening favored majority positions under Controversy and the combined treatment, while cross-treatment comparisons found no added strengthening from combining signals.Under Controversy, strengthening was 35.1% for majority versus 14.4% for minority positions; under Controversy+Certainty, it was 34.9% versus 10.9% when in-group certainty was at least as high, and 22.5% versus 9.7% when lower. Only one cross-treatment contrast was significant: Certainty alone versus the combined minority/high-in-group-certainty cell, 32.4% versus 10.9%.Source: §C.3.2 · p. 73, Table 31 · p. 73, Table 32 · p. 73

C.3.3 Regression robustness: continuous treatment variables versus binary social-position variables

  • The continuous treatment-information block improved held-out mean log-likelihood by 0.0223, exceeding the binary social-position block’s 0.0181 gain by 0.0042.Continuing greedy ablation from the continuous block reproduced the primary covariate selection and final score (−0.4567).Source: Table 33 · p. 74, §C.3.3 · p. 74

C.4 Preregistered regression models for verdict and confidence change

  • Separate preregistered verdict-change and confidence-change models agreed with the combined weakening and strengthening models on the directions and similar significance of the social-information effects.The reported signals were disagreement rate, in-group certainty, and out-group certainty.Source: §C.4 · p. 75

C.4.1 Verdict-change regression

  • The verdict-change regression modeled whether a participant changed their initial verdict, using treatment indicators, interactions with disagreement and certainty signals, and selected pretreatment covariates.The selected covariates included GDMS subscales, intellectual humility, sex, grouped nationality, and age.Source: §C.4.1 · p. 75

1) Ablation.

  • For verdict change, the best model was the GLM without dilemma factors, selected at step 5 with GDMS, Nationality, Sex, intellectual humility, and Age (mean log-likelihood = −0.1992).GDMS and grouped Country/Nationality were the most consistent predictors; Topic, raw nationality variables, and participant-level random effects impaired out-of-sample fit.Source: 1) Ablation · pp. 75–76, Table 35 · p. 77

2) Best Model - Full Coefficient Table

  • Disagreement sharply increased verdict-change odds in Controversy (OR = 86.59) and the combined condition (OR = 54.60).In certainty-only trials, higher in-group certainty reduced verdict-change odds (OR = 0.25), while out-group certainty increased them (OR = 5.32); the out-group effect vanished in the combined condition. Initial confidence was negatively associated with verdict change (OR = 0.15). Spontaneous style and male sex also predicted higher odds of changing; neither intellectual humility nor age was significant.Source: 2) Best Model - Full Coefficient Table · pp. 76–77, Table 36 · p. 78

C.4.2 Confidence-change regression … C.5.2 Willingness to provide a written explanation

Among participants who kept their verdict, social information shifted confidence: disagreement lowered it, while certainty raised confidence when expressed by like-minded users. The section also reports how written explanations were filtered and who chose to provide them.

C.4.2 Confidence-change regression

  • GDMS entered first and Personality second in every forward-selection specification; other tested blocks did not improve fit, and raw geographic effects were unstable or harmful.GDMS was the strongest decision-relevant single block; Personality improved prediction in all four specifications. Topic and participant-level random intercepts impaired fit, and raw geographic variables were excluded from the candidate pool.Source: §C.4.2 · pp. 79–80, Table 37 · p. 80, Table 38 · p. 81
  • β = −0.186 (p < 0.001): greater disagreement reduced confidence; in the combined condition, in-group certainty still raised confidence, but out-group certainty was not significant.In the Certainty condition, in-group certainty raised confidence (β = 0.108, p < 0.001), while out-group certainty lowered it (β = −0.050, p = 0.008). The analysis concerns responses where verdicts held; confidence change was measured as signed post-treatment minus pre-treatment certainty.Source: §C.4.2 · pp. 79–80, Table 39 · p. 82
  • β = −0.220 (p < 0.001): higher initial confidence was associated with a decrease in confidence change; Intuitive style raised confidence and Dependent style lowered it.Intuitive style: β = 0.038 (p < 0.001); Dependent style: β = −0.046 (p < 0.001). Emotional Stability had a small positive association (β = 0.023, p = 0.026); other listed styles and Big Five traits were not significant.Source: §C.4.2 · p. 80, Table 39 · p. 82
  • The final confidence-change model was a Gaussian GLM without dilemma factors, with GDMS and Personality, reaching mean log-likelihood 0.6086 (SE = 0.0175).The glmmTMB specification matched that value, while the two dilemma-absorbing specifications reached 0.5873; only GDMS and Personality improved out-of-sample prediction across specifications.Source: §C.4.2 · pp. 79–80, Table 38 · p. 81

C.5 Exploratory analyses: written explanations, dilemma consequences, and participant feedback

  • The exploratory analyses cover explanation quality and participation, reasoning for judgment change, references to treatment information, dilemma consequences, and survey feedback.The analyses first filter optional explanations, then model who provided them, classify their content, examine consequences linked to judgment change, and review optional survey feedback.Source: §C.5 · p. 83

C.5.1 Quality filtering of written explanations

  • GPTZero flagged 21 of 1,542 explanation providers (1.4%); after manual review, 14 long, high-probability cases were removed, leaving 1,528 participants and 2,744 explanations.Seven flagged responses under 250 characters were retained. The cleaned set was used for subsequent analyses.Source: §C.5.1 · p. 83
  • 2,744 of 4,318 responses (63.5%) included an explanation, with condition rates ranging from 60.8% in Control to 66.0% in Controversy.Rates were 63.9% in Certainty and 63.4% in Controversy + Certainty.Source: §C.5.1 · p. 83

C.5.2 Willingness to provide a written explanation

  • Of 4,318 responses, 2,744 (63.5%) included a written explanation and 1,574 (36.5%) did not.This section asks which participants chose to provide an explanation.Source: §C.5.2 · p. 84

C.5.2.1 Model Specification … Task 1: General reasoning for change.

The section models who provides written explanations and describes how those explanations were categorized, validated, and distributed across reasoning subtypes. Treatment indicators mostly did not predict explanation rates, while the annotation results show a mix of internal, situational, and social reasoning.

C.5.2.1 Model Specification

  • Age was the strongest predictor: older participants provided explanations more often (β = 0.273, p < 0.001).Intuitive style (β = 0.164, p < 0.001) and intellectual humility (β = 0.174, p < 0.001) were positive predictors; Rational style (β = −0.390, p < 0.001), Conscientiousness (β = −0.074, p = 0.021), and Emotional Stability (β = −0.063, p = 0.029) were negative predictors.Source: §C.5.2.1 · p. 84
  • Age gave the largest single-block gain in both specifications (∆¯ℓ= +0.0077 in the GLM and +0.0072 in glmmTMB).GDMS ranked second and grouped Nationality third in both; Personality reduced fit in the GLM (∆¯ℓ= −0.0007) but improved it in glmmTMB (∆¯ℓ= +0.0010), while raw geographic variables reduced fit sharply.Source: §C.5.2.1 · pp. 84–85, Table 42 · p. 85
  • Treatment indicators did not predict explanation rates, but under Controversy, higher disagreement increased the chance of providing an explanation (β = 0.846, p = 0.013).The Controversy, Certainty, and combined-condition indicators had p values of 0.484, 0.114, and 0.137, respectively; certainty and combined-condition slopes were not significant.Source: §C.5.2.1 · p. 85
  • The selected glmmTMB model accepted seven covariate blocks and finished at ¯ℓ= −0.6380, versus −0.6403 for the GLM.The dilemma-level variance was 0.026 (SD = 0.16), so the final model retained a dilemma random intercept.Source: §C.5.2.1 · p. 84, Table 43 · p. 86

C.5.3 Written explanations

  • The explanations were coded for reasoning subtype and stance toward each treatment variable.The paper describes annotation and validation for both tasks before reporting general-reasoning and treatment-reference results.Source: §C.5.3 · p. 88, §C.5.3.1 · p. 88

Task 1: General reasoning for change.

  • For 2,744 explanations, an LLM-assisted scheme reduced fifteen initial patterns to a two-level taxonomy with four broad reasoning types.The scheme includes no explanation, personal-based values or experience, social influence, and situation-based reasoning; human validation on more than 100 explanations found no additional categories were needed.Source: Task 1: General reasoning for change · p. 88
  • Moral conviction was the most common subtype (60.28%), followed by contextual or procedural reasoning (36.70%) and fairness, rights, or responsibility (34.62%).Harm-related reasoning appeared in 21.79% of cases, conformity in 13.05%, resistance in 8.35%, no meaningful explanation in 8.05%, and lived experience in 3.97%; every explanation contained at least one interpretable element.Source: Table 45 · p. 90, Task 1: General reasoning for change · p. 90
  • Annotation alignment with an expert reached macro-averaged F1 = 0.69 and weighted-averaged F1 = 0.68, exceeding the random baseline macro F1 = 0.19.Conformity (F1 = 0.87) and resistance (F1 = 0.85) aligned most strongly; moral conviction (F1 = 0.59) and contextual reasoning (F1 = 0.62) showed moderate agreement.Source: Task 1: General reasoning for change · p. 90, Table 46 · p. 90

Task 2: Treatment reference and stance.

The section defines stance toward social-treatment information independently of judgment change, validates LLM annotations against expert labels, and applies the prompt to all 2,744 explanations.

Task 2: Treatment reference and stance.

C.5.3.2 Results: General reasoning for change · C.5.3.3 Results: Treatment reference and acceptance

Treatment information reshaped both participants’ judgments and their explanations: conformity became prominent, while maintainers and strengtheners relied less on situational reasoning. When both signals were shown, participants referenced disagreement more often than either certainty signal.

C.5.3.2 Results: General reasoning for change

  • Across all three treatments, participants who weakened or strengthened their position more often provided no written explanation than control participants, while maintainers did so less often.All differences were significant: pBH < .01 for weakened and strengthened responses, and pBH < .001 for maintained responses.Source: §C.5.3.2 · p. 94
  • Among participants who weakened their position, conformity appeared in 38.4–50.0% of treatment explanations versus 0.0% in control, the only significant subtype difference.All treatment-control comparisons had pBH < .001.Source: §C.5.3.2 · p. 94
  • Among maintainers, conformity and resistance increased in every treatment, while fairness and contextual reasoning decreased in every treatment relative to control.Conformity was 6.7–9.4% versus 0.5%, and resistance 12.1–14.3% versus 1.4%; moral conviction rose only with Controversy + Certainty (69.9% versus 62.4%).Source: §C.5.3.2 · pp. 94–95
  • Among strengtheners, conformity rose from 4.0% in control to 45.9–63.6% across treatments, while fairness and contextual reasoning declined significantly.Moral conviction declined under Controversy and Controversy + Certainty, and harm-related reasoning declined only under Controversy + Certainty.Source: §C.5.3.2 · pp. 94–95
  • Across change types, treatment information activated conformity-based reasoning, nearly absent in control, and maintainers and strengtheners relied less on situation-based reasoning.The results suggest that social signals substituted social reference for moral and situational engagement.Source: §C.5.3.2 · p. 94, Table 49 · p. 95

C.5.3.3 Results: Treatment reference and acceptance

  • Acceptance was assessed among participants who referenced a signal, with Table 50 reporting accept/reject counts and Table 51 reporting pairwise tests; the supplied prose gives no acceptance comparison.Source: §C.5.3.3 · p. 96, Table 50 · p. 96, Table 51 · p. 96
  • When both signals were shown, participants referenced disagreement more often than in-group certainty or out-group certainty: 29.1%, 10.5%, and 5.1%, respectively.With Certainty alone, in-group certainty was referenced more often than out-group certainty (24.4% versus 17.2%); with Controversy alone, disagreement was referenced in 31.9% of comments.Source: Table 50 · p. 96, §C.5.3.3 · p. 96

C.5.3.4 Participant-clustered regression robustness check. … Task 2: Affected party and vulnerability.

Participant-clustered regressions preserved the main ordering of attention and acceptance, while follow-on sections describe exploratory dilemma annotations and their reliability. Across treatments, people who engaged with a signal usually accepted it, and disagreement and in-group certainty tended to move judgments in opposite directions.

C.5.3.4 Participant-clustered regression robustness check.

  • All four reference-rate contrasts remained significant; in-group certainty was accepted more than out-group certainty in both Certainty and combined conditions.In the combined condition, in-group certainty was accepted more than disagreement, while acceptance of disagreement and out-group certainty did not differ.Source: Table 52 · p. 97, §C.5.3.4 · p. 97
  • Across treatments, most comments engaging with a signal accepted it, while comments rejecting a signal almost always maintained their judgment.Acceptance ranged from 60% to 81%. Accepted disagreement more often weakened than strengthened judgment (Controversy: 31.2% vs. 22.9%; combined: 25.2% vs. 21.4%), whereas accepted in-group certainty more often strengthened than weakened it (Certainty: 30.2% vs. 8.7%; combined: 48.3% vs. 6.9%).Source: §C.5.3.4 · pp. 97–98
  • Comments most often mentioned in-group with out-group certainty in Certainty, but disagreement alone or with in-group certainty in the combined condition.The respective shares were 68%, 65%, and 28%; out-group certainty was rarely discussed on its own.Source: §C.5.3.4 · p. 97, Figure 18 · p. 98

C.5.4 Dilemma consequences

  • The dilemma-consequence analyses explore whether annotated severity, affected parties, and vulnerability are associated with weakening or strengthening; they are exploratory, not confirmatory.Source: §C.5.4 · p. 98

C.5.4.1 Annotation and validation

  • Two post-text coding tasks assessed severity and affected parties or vulnerability, with annotations made blind to outcomes and three independent runs per post.The model saw only each post, not Reddit tallies or participant responses; the runs were aggregated using task-specific rules.Source: §C.5.4.1 · p. 98

Task 1: Severity.

  • Severity was rated as stakes independent of blame on a 1–7 scale, and the three run ratings were averaged.The earlier rubric was retired because its dimensions were largely collinear with severity, two harm labels were near-constant, and its single-select affected-party scheme could not represent multiple groups.Source: Task 1: Severity · p. 98

Task 2: Affected party and vulnerability.

  • Affected parties were coded as a multi-select set, vulnerability as a separate single label, and both outputs were aggregated by majority vote across three runs.Each affected-party category was retained if selected in at least two runs; vulnerability took the modal label, and 126 of 135 posts had unanimous vulnerability labels.Source: Task 2: Affected party and vulnerability · pp. 99–100
  • Severity ratings showed high across-run agreement (ICC(2, 1) = 0.96), and vulnerability labels were unanimous for 126 of 135 dilemmas.Human annotator mean F1 scores were 0.91 for affected party, 0.83 for any vulnerable person, 0.88 for a child, and 0.88 for pairwise severity; model F1 scores against those labels were 0.92, 0.71, 0.78, and 0.85, respectively.Source: Task 2: Affected party and vulnerability · p. 100

C.5.4.2 Results: the consequence profile of dilemmas

The 135 dilemmas ranged from minor everyday disputes to serious, lasting harms, with many involving family or vulnerable people. Severity varied across the corpus and was only weakly related to vulnerability or stranger involvement.

Results: the consequence profile of dilemmas

  • Authors were implicated in 99% of dilemmas, and 46% affected someone identifiably vulnerable.Beyond the author, affected parties included family (73%), strangers (22%), ongoing contacts (15%), and friends (14%); children were the most common vulnerable party (39%).Source: §C.5.4.2 · p. 100, Figure 19 · p. 101
  • Severity correlated weakly with vulnerability (r = 0.22) and modestly negatively with stranger involvement (r = −0.31).Dilemmas affecting strangers never exceeded severity 5.0, whereas those involving family or children spanned the full severity range.Source: §C.5.4.2 · p. 100
  • Severity spanned the 1–7 scale, with a median of 4.0 and an interquartile range of 3.0–5.0.Nineteen dilemmas were rated 2 or below, while 48 were rated 5 or above; high-severity cases included child welfare and family rejection, while low-severity cases included queues and seating.Source: §C.5.4.2 · p. 100, Figure 19 · p. 101

C.5.4.3 Results: consequences and judgment revision · C.5.5 Participant feedback

Dilemma-level weakening and strengthening rates were largely similar across stake and consequence categories, with two exploratory contrasts. Among feedback givers, positive and reflective responses were common, though optional feedback may not represent the full sample.

C.5.4.3 Results: consequences and judgment revision

  • Weakening was the share of all responses that weakened, whereas strengthening was measured only among responses with room to strengthen.Rates were collapsed to one pair per dilemma so each dilemma counted once; overlapping affected-party categories were compared on disjoint subsets, excluding the near-constant author category.Source: §C.5.4.3 · p. 101
  • Most stake-feature categories showed little association with dilemma-level revision rates; family dilemmas weakened more than friend dilemmas, while child dilemmas strengthened less than those involving nobody vulnerable.Both contrasts were treated as exploratory, not confirmatory; the friends group had only nine dilemmas, and each rate was based on 6–10 responses per condition.Source: Figure 21 · p. 103, §C.5.4.3 · p. 103, Table 56 · p. 104
  • MORALMOMENTS dilemmas spanned a wide severity range but clustered at medium to high stakes; family or partners were common affected parties, and almost half involved a vulnerable person.Severity was higher when vulnerable people were involved (median 4.0 vs 3.0) and lower when strangers were involved (median 3.0, never above 5.0).Source: Figure 20 · p. 102, §C.5.4.3 · p. 103

C.5.5 Participant feedback

  • After quality filtering and excluding neutral or empty entries, 458 meaningful feedback responses—21.2% of the full sample—were analyzed across eight categories.A response could be assigned more than one category.Source: §C.5.5 · p. 105, Figure 22 · p. 105
  • Positive Experience was the most common feedback category at 68.6%, followed by Polite Social Closing at 38.4% and Learning, Reflection and Insight at 20.3%.Positive Experience alone was the largest combination at 27.3%; most other frequent combinations paired a positive category with another category.Source: §C.5.5 · p. 105, Figure 22 · p. 105
  • Feedback often praised the real online-community material or the chance to reflect, while a small number raised design concerns, including the second judgment page.Because feedback was optional, these patterns may not represent the full sample.Source: §C.5.5 · p. 105, Table 57 · p. 106
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