Bibliographic record
Abstract
In Brief In this article, we argue in favor of quality assessment for qualitative studies and propose using a strategy we have labeled recognizability to assess external validity and facilitate knowledge transfer. To test our idea, we gathered data about recognizability in relation to a specific study on facial disfigurement. Four categories were identified: full recognition; partial recognition; recognition in others; and no recognition. In this article, we show how we used these categories both to evaluate the quality of our study and to assess its external validity. We also discuss the implications of recognizability for knowledge transfer. In this paper we propose using a strategy we have labelled recognizability to assess external validity and facilitate knowledge transfer. To test our idea, we gathered data about recognizability in relation to a specific study on facial disfigurement. Four categories were identified; Full recognition, Partial recognition, Recognition in others and No recognition. www.advancesinnursingscience.com
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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 | 0.181 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".