El siguiente paso en el desarrollo de los repositorios: integración con la infraestructura institucional de gestión de la información científica
Bibliographic record
Abstract
Dídac Martínez, Director de l'Àrea de Serveis Universitaris de la UPC, va esmentar en la seva introducció una sèrie de 10 prioritats per al \ndesenvolupament dels dipòsits institucionals, de les quals la desena era la integració dels repositoris en la infraestructura institucional de gestió de la informació científica. A més de citar diverses iniciatives de integració en aquest sentit -tant per investigació com per a continguts docents- a la Universitat Politècnica de Catalunya, la presentació va esmentar NARCIS com a model, un sistema CRIS d'àmbit nacional desenvolupat a Holanda per una sèrie d'universitats (que comparteixen Metis com a sistema CRIS institucional), NWO i el KNAW.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".