La place des enseignements artistiques en 2030
Bibliographic record
Abstract
Dans l’ouvrage Le futur de l’éducation et des compétences. Projet Éducation 2030 (OCDE, 2018), plusieurs défis sont identifiés pour l’école de demain. Qu’ils soient d’ordre social, économique ou environnemental, ces défis questionnent les acteurs du monde de l’éducation et de la formation sur leur capacité à accompagner les élèves ou les étudiants à devenir les citoyens de demain. Quelles seront les compétences, les attitudes et les valeurs qui leur seront nécessaires en 2030 ? Comment les systèmes éducatifs et de formation peuvent-ils s’adapter aux exigences de la prochaine décennie ? Les enseignements artistiques ont-ils le potentiel d’apporter des solutions créatives et innovantes ? Comment les recherches en éducation sur les pratiques artistiques contribuent à créer et développer une réflexion sur l’éducation de demain ?
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 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".