Qualitative Research in the Age of LLMs: A Human-in-the-Loop Approach to Hybrid Thematic Analysis
Bibliographic record
Abstract
This paper introduces a hybrid (deductive-inductive) human-in-the-loop (HHitL) framework for thematic analysis (TA), leveraging large language models (LLMs) while prioritizing human interpretation and decision-making.Grounded in reflexive TA, our resulting six-stage framework is guided by design principles of interoperability, human-LLM transactivity, scalability, and transparency.This method enables mixed-methods displays (e.g., network graphs, code frequency/diversity/magnitude metrics) supporting productive data triangulation.We demonstrate this HHitL-TA approach by analyzing engineering students' motivations based on Social Cognitive Career Theory (SCCT), illustrating the framework's application, principles, and triangulation potential.While acknowledging potential risks, the HHitL design actively mitigates passive researcher roles through transactivity and transparency, promoting humans as generative, active directors of the analysis.This work contributes a structured methodology for rigorous, scalable qualitative inquiry using LLMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.216 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".