Pour une histoire de l’inventaire des objets de Polynésie française dispersés dans les musées métropolitains : le rôle déterminant d’Anne Lavondès
Bibliographic record
Abstract
L’article, qui s’appuie sur des entretiens entre Anne Lavondès et Véronique Mu-Liepmann réalisés en 2020 et 2021, retrace d’abord l’histoire des objets polynésiens dispersés, à travers les travaux précurseurs de ceux d’Anne Lavondès : les études de W.M.T. Brigham (fin xixe s.), Karl von den Steinen (fin xixe-début xxe), Margaret Titcomb (1937) et enfin Marie-Charlotte Laroche (après 1945). Il rend ensuite compte de l’apport d’Anne Lavondès, dont le travail a sensibilisé conservateurs et responsables de collections à ces ensembles patrimoniaux océaniens et polynésiens, tout en permettant aux Polynésiens de se réapproprier des éléments de leur culture matérielle conservés dans les musées métropolitains. Il retrace enfin les travaux effectués depuis les années 1990, dont ceux de Roger Boulay, et évoque l’inventaire lancé par la Polynésie française depuis 2016 pour constituer une indispensable base de données de ses collections dispersées.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".