Le refus, une stratégie de développement des collections muséales
Bibliographic record
Abstract
En s’appuyant sur l’expérience de trois musées de société du Québec, cet article aborde le collectionnement muséal sous l’angle du refus. Il présente d’abord les défis qui conditionnent aujourd’hui le développement des collections et qui incitent les musées à orienter leurs pratiques autour de ce geste. Supportés par un discours refusant la fin des acquisitions, le refus de prendre et le refus de garder s’imposent comme des pratiques incontournables. Présenter le refus comme une modalité effective de développement des collections permet ensuite d’ouvrir la discussion sur les mécanismes ayant mené à son adoption et sur les positions institutionnelles qu’il éclaire. Si les pratiques de refus peuvent donner une image d’un musée de société détaché de ses collections, cette étude révèle plutôt son engagement envers la poursuite de leur développement.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".