La recherche scientifique au temps des réformes économiques : La place des sciences de l’éducation dans le débat actuel
Bibliographic record
Abstract
L’essor des pays industrialises a ete particulierement marque par le developpement technoscientifique, teintant et transformant de facon permanente les societes a l’interieur desquelles a pris naissance le germe du progres (Barma & Guilbert, 2006). En effet, la definition du lien science-technologie-societe ne date pas d’hier et a couramment ete decrite par son abondant reseau de relations (Aikenhead, 1992; Fourez, 1994; Gardner, 1997). A titre d’exemple, les gouvernements ont souvent, par le passe, fait reposer leurs politiques sur l’avis de scientifiques pour en legitimer la pertinence. En contrepartie, il incombait aux scientifiques de communiquer leurs decouvertes, afin d’en partager les retombees et d’en debattre avec l’ensemble des publics composant la societe (Grise, 2013). Toutefois, il est releve qu’actuellement, au Quebec et dans l’ensemble du Canada, le developpement des differents champs scientifiques traverse une periode charniere de son histoire. Aujourd’hui, plus que jamais, on remarque que les sciences ne sont pas exemptes des decisions humaines qui les faconnent (Fourez, 2002). La tension sociopolitique exercee sur ces dernieres, a fortiori en matiere de financement et de diffusion, est desormais en mesure d’inflechir leurs courses, de telle sorte qu’elles s’en voient alterees, voire compromises.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".