Dépenses intra-muros au titre de la recherche et développement des organismes privés sans but lucratif, selon le domaine de la recherche et développement et la nature de la recherche et développement
Bibliographic record
Abstract
Ce tableau contient 48 séries, avec des données pour les années 2014 - 2015 (il n'y a pas nécessairement de données pour toutes les années pour l'ensemble des combinaisons). Ce tableau contient des données telles que décrites par les dimensions suivantes (Les combinaisons ne sont pas toutes disponibles) : Géographie (1 élément : Canada) Nature de la recherche et développement (4 éléments : Total, recherche; Recherche fondamentale; Recherche appliquée; Développement expérimental) Domaine de science ou technologie (12 éléments : Sciences naturelles et génie; Sciences exactes et naturelles; Génie et technologie; Logiciels relatifs aux sciences et technologies; ...).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".