Bibliographic record
Abstract
In this article, I trace the historical groundings of what have become methodological conventions in the use of qualitative approaches to answer questions arising from the applied health disciplines and advocate an alternative logic more strategically grounded in the epistemological orientations of the professional health disciplines. I argue for an increasing emphasis on the modification of conventional qualitative approaches to the particular knowledge demands of the applied practice domain, challenging the merits of what may have become unwarranted attachment to theorizing. Reorienting our methodological toolkits toward the questions arising within an evidence-dominated policy agenda, I encourage my applied health disciplinary colleagues to make themselves useful to that larger project by illuminating that which quantitative research renders invisible, problematizing the assumptions on which it generates conclusions, and filling in the gaps in knowledge needed to make decisions on behalf of people and populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.583 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.017 | 0.197 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.016 | 0.043 |
| Insufficient payload (model declined to judge) | 0.003 | 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".