Bibliographic record
Abstract
Le terme « infrastructure » évoque spontanément les équipements \nroutiers, ferroviaires, maritimes, le système d’électricité ou le réseau \nhydraulique. En science, les infrastructures de recherche réfèrent aux \ngrands équipements scientiiques (observatoires astronomiques, synchrotrons, \nréseaux de surveillance de l’environnement), aux collections (musées d’histoire naturelle) ou encore aux infrastructures informationnelles \n(Internet et grandes bases de données). Sur le plan théorique, \nla notion d’« infrastructure sociotechnique » a été proposée au milieu \ndes années 1990 par Star et Ruhleder pour étudier l’infrastructure d’un \npoint de vue sociologique, c’est-à-dire du point de vue des pratiques \nassociées à leur développement, cherchant ainsi à dépasser les visions \npurement techniques (voir Objet technique). Les études d’infrastructure \n(infrastructure studies) se sont développées autour de cette notion.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".