Bibliographic record
Abstract
La crise de la Covid-19 a mis en lumière l’importance de la parole des chercheurs dans la prise de décision politique. Qu’en est-il dans le domaine de l’éducation ? Comment s’articulent les relations entre la recherche en éducation, la décision politique et l’action de terrain ? Quel est le pouvoir des différents acteurs et où est-il situé ? Ce numéro analyse des contextes variés : France, Chine, Mexique, Russie, Suède ou encore Pays de Galles ou Québec, et élargit la réflexion au cas de l’Afrique et des pays arabes. Il met en évidence les différents équilibres en place et leur impact sur l’éducation. Une coopération étroite entre les sphères de la recherche, de la décision et de l’action semble plus que jamais nécessaire pour que les systèmes éducatifs puissent répondre aux besoins d’éducation et faire face aux mutations sociétales. Ce dossier tente ainsi de dégager les conditions qui permettraient de créer de l’intelligence collective au bénéfice de la qualité de l’éducation. The Covid-19 crisis has highlighted the importance of the voice of researchers in political decision-making. What about in the field of education? How do the relationships between education research, political decision-making and action on the ground intersect? What is the power of the different players and where is it located? This issue analyses various contexts: France, China, Mexico, Russia, Sweden, Wales and Quebec, and broadens the reflection to the case of Africa and Arab countries. It highlights the different equilibria in place and their impact on education. Close cooperation between the spheres of research, decision-making and action seems more necessary than ever in order for education systems to be able to meet educational needs and face up to societal changes. This dossier thus attempts to tease out which conditions would make it possible to create collective intelligence to support the quality of education. La crisis de la Covid-19 ha puesto en evidencia la importancia de la palabra de los investigadores en la toma de decisión política. ¿Se puede llegar a semejante constatación en el campo de la educación? ¿Cómo se articulan las relaciones entre la investigación en educación, la decisión política y la acción en el terreno? ¿Cuál es el poder respectivo de los diferentes actores y dónde se sitúa? Este número analiza unos contextos variados: Francia, China, México, Rusia, Suecia, Gales o Quebec, y amplia la reflexión a los casos de África y de los países árabes. Pone en evidencia los distintos equilibrios instaurados y su impacto en la educación. Una cooperación estrecha entre las esferas de la investigación, de la decisión y de la acción parece más que nunca necesaria para que los sistemas educativos puedan responder a las demandas de educación y se enfrenten con las mutaciones de las sociedades. Este dossier intenta así identificar las condiciones que permitirían crear una inteligencia colectiva en beneficio de la calidad de la educación.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.118 | 0.069 |
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".