Bibliographic record
Abstract
L’utilisation d’Internet dans la recherche scientifique connaît une forte croissance. Comparé aux méthodes plus traditionnelles, le Web offre l’accès à un grand nombre de participants à un coût réduit, ce qui permet une plus grande démocratisation de la recherche. Ce mode de collecte comporte toutefois un certain nombre de défis liés à la déontologie de la recherche, à la conception du questionnaire, à l’échantillonnage et aux instruments de mesure. Cet article présente dans un premier temps les avantages de la collecte de données en ligne. Il précise ensuite les principaux enjeux de cette méthode. La présentation de chacun de ces enjeux est accompagnée d’une description de la démarche effectuée dans le cadre d’une étude quantitative réalisée auprès de gestionnaires.
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 | 0.152 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".