Bibliographic record
Abstract
Le lexique employé pour décrire et théoriser les transferts et les mélanges culturels a connu une inflation de termes depuis un demi-siècle : acculturation, transculturation, interculturation, traduction, métissage, créolisation et hybridation. Cet article vise à mieux comprendre le pourquoi de ce lexique en reconstituant l’évolution de ces mots, le contexte sociopolitique de leur émergence et les tensions idéologiques qui agissent sur leur sens. En dépit de la multiplication des termes pour dire les métissages, on y constate une redondance sémantique et des restrictions de sens, voire une certaine pauvreté conceptuelle dans l’usage des mots. Il y a une forte tendance à décrire et à analyser des phénomènes de fusion culturelle, comme si les cultures devaient obligatoirement se rencontrer et se mélanger. Or, cet article démontre que les rapports entre le soi et l’autre sont multiples et variés, allant du refus catégorique de contacts à l’assimilation volontaire.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".