Du queer au musée : Réflexions méthodologiques sur la manière d’inclure le queer dans les collections muséales
Bibliographic record
Abstract
À partir de son expérience personnelle comme commissaire d’exposition, de constats effectués au cours de l’été 2008 en Suède et des travaux de différents chercheurs, l’historien d’art Patrik Steorn formule une critique queer du musée permettant non seulement de considérer une plus grande multitude de sexualités et de genres (homo-, bi-, trans-), mais aussi de dénoncer l’hétéronormativité des regards, des discours et des pratiques en place ; le queer relevant autant de l’identité que d’un outil analytique critique de cette même identité. Il part d’un constat assez simple : celui de la non-prise en compte des critères relatifs aux sexualités (hétéro-, homo-, bi-, trans-) dans les bases de données des collections muséales qu’il étudie et explique alors comment ces critères de recherche reconduisent les catégories établies et produisent du sens quant à la manière (partielle et partiale) de voir et de vouloir montrer du musée.
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 | 0.105 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.012 | 0.063 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".