Bibliographic record
Abstract
Sexy, les données qualitatives ? C’est ainsi que les décrivent Miles et Huberman (1994, p. 1), des chercheurs qualitatifs réputés ! En effet, nombre de lectrices et de lecteurs habitués aux sciences dites naturelles disent que lire un article qualitatif, c’est un peu comme entendre « la voix » des participantes et participants, comme se faire raconter leur vision personnelle des choses et des événements. Les résultats qualitatifs « sonnent vrai » et résonnent avec le vécu et l’expérience personnelle des individus qui les lisent. Ils paraissent ainsi habituellement plus convaincants et moins arides que les résultats d’analyses de variance (ANOVA), de régressions ou d’analyses acheminatoires (Miles & Huberman, 1994). Ce n’est pas surprenant, puisque les recherches qualitatives se basent souvent sur les mots, sur le langage, des « outils » qu’on utilise tous les jours pour communiquer avec nos semblables.
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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 | 0.252 | 0.300 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".