Criteria for quality in qualitative research and use of freedom of information requests in the social sciences
Bibliographic record
Abstract
Access to information (ATI) and freedom of information (FOI) requests are an under-used means of producing data in the social sciences, especially across Canada and the United States. We use literature on criteria for quality in qualitative inquiry to enhance ongoing debates and developments in ATI/FOI research, and to extend literature on quality in qualitative inquiry. We do this by building on Tracy’s (2010) article on criteria for quality in qualitative inquiry, which advances meaningful terms of reference for qualitative researchers to use in improving the quality of their work; and illustrating these criteria using examples of ATI/FOI research from our own work and from others’ in Canada, the United States, and the United Kingdom. We argue that, when systematically designed and conducted, ATI/FOI research can prove extraordinary in all eight of Tracy’s criteria.
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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 | 0.630 | 0.761 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.007 | 0.007 |
| 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".