Bibliographic record
Abstract
For the past several years faculty at researchuniversities have been publishing more and morejournal articles. The proposed poster presents sets ofdata that show precisely how much more faculty arepublishing. The institutional figures unequivocallydemonstrate a trend that is rather stark. Moreover,the data show that citations to the work of theuniversities’ faculty demonstrate a similar trend ofincrease. Not only will the aggregate data bepresented, tables will show the comparative rankingsof the institutions over four time periods.Depuis plusieurs années les professeurs dans lesuniversités de recherche publient de plus en plusd’articles dans des revues savantes. L’afficheproposée présente des ensembles de données quimontrent avec précision la quantité supplémentaired’articles publiés par les professeurs. Les chiffresinstitutionnels démontrent sans équivoque unetendance qui est frappante. En outre, les donnéesmontrent que les citations des travaux deschercheurs montrent une tendance similaire àl’augmentation. Les données agrégées serontprésentées, ainsi que les tableaux montrant lesclassements comparatifs des institutions sur quatrepériodes de temps.
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 | 0.008 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.044 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".