Au carrefour des possibles. Quelles innovations sociales contre les injustices sociales, environnementales et épistémiques?
Bibliographic record
Abstract
Ce numéro spécial du CJNSER s'appuie sur le 6e Colloque international du Centre de recherche sur les innovations sociales (CRISES) qui s'est tenu au printemps 2021, en pleine pandémie, sous le titre « Au carrefour des possibles.Quelles innovations sociales contre les injustices sociales, environnementales et épistémiques?» Ce colloque visait à comprendre l'articulation entre les différentes crises en cours (environnementale, socioéconomique, sanitaire, politique) et le rôle des innovations sociales qui naissent pour y faire face.L'objectif était également de mieux distinguer, parmi ces dernières, celles qui contribuent à lutter contre les injustices sociales, environnementales et épistémiques.This special issue of CJNSER draws from the 6th International Conference of the Centre for Research on Social Innovations (CRISES), which took place in spring 2021, in the middle of the pandemic, under the title "At the crossroads of possibilities.What social innovations against social, environmental, and epistemic injustices?"This conference sought to understand the interconnections between the different crises taking place (environmental, socioeconomic, sanitary, political) and the role of social innovations that arose to deal with them.The objective was to better distinguish, among the innovations, the ones that could best contribute to the fight against social, environmental, and epistemic injustices.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.031 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".