Bibliographic record
Abstract
Cet article met au premier plan la structure fondamentale de differents indicateurs relatifs. Ceci mene a un dispositif puissant pour construire de nouveaux indicateurs. Une attention particuliere est portee a l'impact relatif d'une revue a l'interieur d'un ensemble de revues : une meta-revue si l'on veut. Des exemples des facteurs d'impact relatifs sont calcules pour un groupe de revues en sciences de l'information, et pour un groupe de revues en gestion. Les avantages des indicateurs d'impact relatifs sont releves. Ces indicateurs sont etudies plus a fond dans le contexte de l'analyse de regression. En conclusion, il est demontre que, compare au facteur d'impact renormalise de Ramirez, Garcia, et Del Rio (2000), le facteur d'impact relatif est plus apte a detecter les changements dans les contributions relatives des revues a l'interieur d'un ensemble de revues.
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 | 0.075 | 0.201 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.032 | 0.043 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".