Qualitative Methods and AI: Friends or Foes?
Bibliographic record
Abstract
With the rapid development in artificial intelligence (AI), specifically the capabilities of large language models (LLMs), the question may not be if but rather how AI will or can enhance the research process. In this panel, we focus specifically on the work of a qualitative researcher. Qualitative methods research is particularly suited for leveraging the power of LLMs, given its focus on words (as opposed to numbers). While these new technologies available may create new opportunities for innovating research methods, it may also challenge the fundamentals of qualitative methods. Therefore, before we can have an opinion on whether AI and qualitative methods are
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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 | 0.322 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.012 | 0.119 |
| Scholarly communication | 0.025 | 0.056 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".