Enhancing the Usefulness of Qualitative Inquiry: Gaps, Directions, and Responsibilities
Bibliographic record
Abstract
Although qualitative researchers have often complained that our research is not funded, that our articles receive unfair reviews, and that our findings are not implemented, as a discipline we have been slow to develop criteria for review of articles, are just beginning to develop criteria for review of proposals, and have not yet suggested mechanisms for the transference of qualitative findings into practice. In this keynote address, using the example of a project conducted at a summer camp for ventilator-dependent children, the author examines these issues of review and utilization and explores the responsibility of qualitative researchers. She argues that by meeting these needs, qualitative researchers will expedite qualitative inquiry and hence will legitimize the discipline.
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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 | 0.782 | 0.728 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.019 | 0.079 |
| Scholarly communication | 0.035 | 0.076 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".