Source-linked AI summary
A Framework For Discussing LLMs as Tools for Qualitative Analysis
James Eschrich, Sarah Sterman
TL;DR
Researchers disagree about whether and how LLMs should support qualitative analysis. The paper reviews philosophy of science and cognitive linguistics to propose a framework based on two questions about model proposals or refutations and direct human checking. It identifies surfacing counter-examples for human review as a promising point of overlap across philosophical perspectives, while the effect on analysis results remains open.
Problem
Researchers disagree about whether, where, and to what extent LLMs should support qualitative analysis.
Source: §1 · p. 1Method
Results
Takeaways & Limitations
Human review of LLM-surfaced counter-examples could support collaborative development of tools and practices across philosophical boundaries.
Source: §4 · pp. 2–3Whether this use positively or negatively affects qualitative-analysis results remains an open question.
Source: §4 · p. 2
Abstract
from arXiv · showhide
We review discourses about the philosophy of science in qualitative research and evidence from cognitive linguistics in order to ground a framework for discussing the use of Large Language Models (LLMs) to support the qualitative analysis process. This framework involves asking two key questions: "is the LLM proposing or refuting a qualitative model?" and "is the human researcher checking the LLM's decision-making directly?". We then discuss an implication of this framework: that using LLMs to surface counter-examples for human review represents a promising space for the adoption of LLMs into the qualitative research process. This space is promising because it is a site of overlap between researchers working from a variety of philosophical assumptions, enabling productive cross-paradigm collaboration on tools and practices.
1 INTRODUCTION
The paper addresses disagreement about whether and how LLMs should support qualitative analysis. It proposes a framework for structuring that discussion and identifies counter-examples for human review as a promising use.
1 INTRODUCTION
- Researchers disagree about whether, where, and to what extent LLMs should or could support qualitative analysis.Emergent work explores deductive coding and inductive thematic analysis.Source: §1 · p. 1
- The paper proposes a framework for discussing LLM use in qualitative analysis, organized around whether an LLM proposes or refutes a model and whether researchers check its decisions directly.It identifies surfacing counter-examples for human review as a promising space for adoption.Source: §1 · p. 1
2 BACKGROUND
The paper frames positivism and constructivism through consistency and congruence, then considers how cognitive linguistics bears on LLM-supported analysis. The paradigms differ in their treatment of models and the unknown, but both attend to models inconsistent with observation.
2.1 Positivism and Constructivism
- The paper uses consistency and congruence, rather than ontology, epistemology, and methodology, to characterize positivism and constructivism, while noting that this two-paradigm framing is limited.The authors describe the paradigms as a continuum and acknowledge that the broader picture is oversimplified and potentially limiting.Source: §2.1 · pp. 1–2
- Positivism seeks congruent models that correspond one-to-one with external reality, making the rejection of observation-inconsistent models relevant.Once congruence is established, the possibilities of consistent and unknown models become irrelevant to this account.Source: §2.1 · pp. 1–2
- Constructivism denies that congruent models can be established and instead explores consistent models while remaining attentive to the unknown.The unknown includes models not yet tested or conceived, as well as models fundamentally unknowable to humans.Source: §2.1 · p. 2
2.2 LLMs and The Implications of Cognitive Linguistics
- Evidence that language understanding draws on cultural and embodied experience challenges LLM use, but positivists focus on consistency of outcomes rather than how claims are produced.They still regard measuring how consistently LLMs reach congruence with reality as important.Source: §2.2 · p. 2
- Constructivists focus on how an LLM produces claims because evaluating them requires understanding that process; the value of LLM-originated claims remains uncertain.The paper presents positivist and constructivist positions as idealized explanatory characterizations, not individuals’ fixed identities across contexts.Source: §2.2 · p. 2
3 A FRAMEWORK FOR DISCUSSING LLMS FOR QUALITATIVE ANALYSIS
The proposed framework organizes discussion of LLMs in qualitative analysis around two questions: whether the LLM proposes or refutes a model, and whether the researcher checks its decision-making directly.
3 A FRAMEWORK FOR DISCUSSING LLMS FOR QUALITATIVE ANALYSIS
- The framework asks whether an LLM is proposing or refuting a qualitative model and whether a human researcher is directly checking the LLM’s decision-making.The first question highlights an asymmetry between affirming and refuting consistency; the second draws attention to human language understanding and the extent to which LLMs support or replace it.Source: §3 · p. 2
4 DISCUSSION
The discussion identifies LLMs surfacing counter-examples for human review as a point of overlap between positivist and constructivist concerns. It considers this use for large datasets while noting that its effect on analysis results remains open.
4 DISCUSSION
- Surfacing counter-examples for human review gives positivists and constructivists a shared set of questions, although they may differ on what evidence indicates inconsistency and in which contexts.Both are interested in discarding models inconsistent with observation.Source: §4 · p. 2
- Having a human researcher judge counter-examples leaves the methodological question of whether this LLM use is useful or effective for qualitative analysis.The paper argues that negating a model is ontologically neutral and that no qualitative-analysis tool is epistemologically neutral.Source: §4 · p. 2
- Researchers could analyze very large datasets by refining an initial traditional-method sample with LLM-surfaced counter-examples for human review.The proposed sequence resembles theoretical sampling used to develop and refine emerging categories [4, p. 205].Source: §4 · p. 2
- Whether LLM-surfaced counter-examples improve or worsen qualitative-analysis results remains an open question.The proposed restriction keeps consistency claims with human researchers and gives them an opportunity to revise a model before an LLM-identified observation leads to its rejection.Source: §4 · p. 2
- The authors present counter-example review as a point of compatibility for collaboratively developing tools and practices across philosophical boundaries.This possibility follows from the shared interest in rejecting models inconsistent with observation.Source: §4 · pp. 2–3
5 CONCLUSION
The conclusion presents a framework for discussing LLMs in qualitative analysis, based on two questions about model proposals and direct human checking. It identifies surfacing counter-examples for human review as a promising area of cross-paradigm overlap.
- The framework asks whether an LLM is proposing or refuting a qualitative model and whether a human researcher directly checks its decision-making.Source: §5 · p. 3
- Using LLMs to surface counter-examples for human review is identified as a promising use within qualitative analysis.Source: §5 · p. 3
- This use offers overlap among philosophical perspectives, and the authors hope the framework supports cross-paradigm discussion of LLMs as tools for qualitative analysis.Source: §5 · p. 3