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Record W4207018365 · doi:10.4000/temoigner.7238

Pédagogie des lieux de mémoire et responsabilisation

2018· article· fr· W4207018365 on OpenAlexaff
Yariv Lapid

Bibliographic record

VenueTémoigner. Entre Histoire et Mémoire · 2018
Typearticle
Languagefr
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Yves Lapid, responsable du département pédagogique du Mémorial du camp de Mauthausen de 2007 à 2013, s’intéresse à la dynamique des visites des lieux de mémoire. Plus particulièrement, il met en cause l’efficience du parcours traditionnel où le guide tient un exposé de deux heures devant un public passif et choqué par la brutalité du récit. Il décide alors d’élaborer une méthode pédagogique qui encourage les visiteurs à découvrir le site avec davantage d’interactivité, à penser sa signification aujourd’hui, et à développer un savoir historique de manière autonome. La tâche du guide-transmetteur est ici limitée à « aider les visiteurs à décrypter ce qu’ils voient » et à stimuler la discussion. L’accès direct à des sources documentaires (des photos et des témoignages écrits), puis l’observation des lieux s’avèrent essentiels. L’auteur souligne d’ailleurs l’importance de déconstruire certains mythes – le « mythe victimaire autrichien » en l’occurrence – et d’illustrer notamment à quel degré le camp de Mauthausen faisait partie intégrante de la société autrichienne de l’époque, et de la vie des villageois.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0060.006
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0210.005

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.

Opus teacher head0.047
GPT teacher head0.285
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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