Identité, diversité et nation dans les musées d’ethnographie en Croatie
Bibliographic record
Abstract
Pendant que la majorité des musées ethnologiques occidentaux cherchent de nouvelles approches au traitement de la diversité culturelle, et ceci non seulement dans les Amériques issues en grande partie de l’immigration en provenant d’une variété de cultures, mais aussi dans les État-nations européens, les musées dans les pays postcommunistes se concentrent sur les études d’une culture nationale. Cependant, il faudrait apporter des nuances à cette dichotomie réductrice qui s’impose au premier regard. En fait, c’est le piège dans lequel tombent le plus souvent les auteurs qui traitent des pays postcommunistes, car ils conservent cette attitude issue de la guerre froide de les observer en bloc et en opposition. En effet, la situation est plus complexe car on y trouve des pratiques et des approches diverses à la fois entre les pays et entre les institutions muséales d’un même pays. Nous aborderons cette problématique en utilisant les exemples des musées ethnographique de Croatie.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".