Bibliographic record
Abstract
Les méthodes d’analyse des données qualitatives et le monde informatique étaient faits pour se rencontrer. Et en effet, la question est d’actualité et les outils informatiques nombreux et avancés. Ce phénomène ne saurait s’estomper, d’autant moins que l’analyse des données secondaires connaît en même temps des développements importants. Mais l’attrait pour les logiciels d’analyse peut devenir tel qu’on ne verrait plus trop à quel titre et pour quelles raisons on pourrait s’en passer. L’article tente de cerner une vision et une pratique de l’analyse qualitative qui, dans son essence, ne se prête pas à l’utilisation d’outils informatiques spécialisés. Il situe sa réflexion dans le cadre de la méthodologie qualitative (démarche qualitative, recherche qualitative, analyse qualitative), plus particulièrement au niveau de l’enquête qualitative de terrain.
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 | 0.179 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".