La mise en récit et la construction de mémoires collectives par les institutions patrimoniales
Bibliographic record
Abstract
La parole du témoin est très présente dans les expositions et les cyberexpositions d’institutions patrimoniales, par exemple sous la forme de publications d’archives ou d’interviews produites en vue de l’exposition. Ces témoignages présentent des récits individuels, subjectifs, au sein d’une institution censément détentrice de savoirs et possédant, aux yeux de la société, une légitimité scientifique. Cela pose alors la question de la place de ces témoignages dans l’exposition. À partir de l’analyse sémio-pragmatique de plusieurs dispositifs, nous avons pu mettre en évidence des stratégies narratives pour exposer des sujets d’histoire récente. Chaque témoignage peut être considéré comme un récit et l’organisation spatiale des récits dans les expositions forme des macro-récits. Chaque internaute-visiteur, par son parcours de visite, compose un macro-récit unique et partiel. Nous avons aussi pu mettre en évidence la logique combinatoire de ces dispositifs.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".