Analyses qualitatives et quantitatives : deux visions, une même science
Bibliographic record
Abstract
Cet article suggère que, malgré bon nombre de plaidoiries qui affirment que les méthodes qualitatives et quantitatives sont antinomiques, ces deux types de méthode ont beaucoup en partage et ils sont tous les deux soumis aux mêmes exigences de scientificité. Non seulement jouent-ils des rôles complémentaires dans les sciences humaines, mais, en plus, 1) leurs analyses doivent toujours avoir quelque représentativité, 2) leurs résultats doivent pouvoir caractériser un ensemble étudié, 3) les caractérisations doivent être modulées par les variations individuelles, 4) leurs analyses doivent établir des liens entre les objets/sujets étudiés, et 5) ces analyses sont soumises à la tension qui s’impose entre l’obligation de regrouper les données et celle d’en respecter les particularités.
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 | 0.220 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.031 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".