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Record W1972785025 · doi:10.7202/030900ar

History in Schools: Reflections on Curriculum Priorities

2006· article· fr· W1972785025 on OpenAlexvenueno aff
David Pratt

Bibliographic record

VenueHistorical Papers · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Depuis nombre d'années déjà, l'enseignement de l'histoire a fait l'objet d'études diverses. On s'est préoccupé tantôt de la matière présentée, tantôt des méthodes utilisées, et on s'est surtout beaucoup interrogé sur la place que l'histoire occupe - ou devrait occuper - dans les programmes scolaires, particulièrement aux niveaux primaire et secondaire. Selon l'auteur, la tâche principale des planificateurs de programmes devrait être d'établir un ordre de priorités au sein des matières enseignées et il estime que le maintien d'une tradition ne devrait, d'aucune façon, constituer une raison suffisante pour garder une matière au programme. A cet égard, le cas de l'histoire est compliqué du fait de son ethnocentrisme reconnu. De plus, de nombreuses questions se posent quant à la capacité des enfants du primaire de comprendre certains des aspects importants de l'histoire et, de même, l'histoire ne semble pas encore avoir de véritable raison d'être au niveau secondaire. L'auteur propose donc que l'enseignement de l'histoire soit abandonné au primaire et que l'on établisse sa raison d'être au secondaire en se basant sur les trois prémisses suivantes: l'histoire peut convenablement servir d'introduction à l'utilisation de témoignages ou d'évidences; la biographie peut contribuer à augmenter la conscience de l'individualité chez l'étudiant; le partage d'expériences constitue la base même de toute identité culturelle.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0180.021
Scholarly communication0.0170.014
Open science0.0020.014
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0240.002

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.

Opus teacher head0.117
GPT teacher head0.360
Teacher spread0.243 · 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 designQualitative
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
Published2006
Admission routes1
Has abstractyes

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