Open Data in Qualitative Research
Bibliographic record
Abstract
There is a growing movement for research data to be accessed, used, and shared by multiple stakeholders for various purposes. The changing technological landscape makes it possible to digitally store data, creating opportunity to both share and reuse data anywhere in the world for later use. This movement is growing rapidly and becoming widely accepted as publicly funded agencies are mandating that researchers open their research data for sharing and reuse. While there are numerous advantages to use of open data, such as facilitating accountability and transparency, not all data are created equally. Accordingly, reusing data in qualitative research present some epistemological, methodological, legal, and ethical issues that must be addressed in the movement toward open data. We examine some of these challenges and make a case that some qualitative research data should not be reused in secondary analysis.
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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 | 0.415 | 0.500 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".