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Record W4294104526 · doi:10.29173/assert42

Teaching history as an interpretation, by using textbooks in a diachronic perspective

2022· article· en· W4294104526 on OpenAlexvenueno aff
Karel Van Nieuwenhuyse

Bibliographic record

VenueAnnals of Social Studies Education Research for Teachers · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsInterpretation (philosophy)ConstructiveRepresentation (politics)NarrativePerspective (graphical)Historical thinkingEpistemologySociologyComparative historical researchSocial history (medicine)HistorySocial sciencePedagogyLiteratureLinguisticsPoliticsVisual artsProcess (computing)Political scienceComputer sciencePhilosophyArtLaw

Abstract

fetched live from OpenAlex

History textbooks play an important role in the social representations of the past circulating within a society. Research shows, however, that textbooks often present their account of the past as 'the truth’: as a representation of what really, actually happened, leaving no room for different interpretations. This is at odds with the essence of history as being a matter of substantiated interpretation and construction, based on historical source analysis and considering multiple perspectives. If we want young people to deal critically with historical representations, it is necessary that they learn to use history textbooks in a critical manner. This article first reports on a diachronic narrative analysis of 20 secondary school history textbook series in Belgium since 1945, specifically focusing on the representation of the Belgian-Congolese colonial past. Afterwards a concrete didactical model is presented about how to transfer the results of this research into educational activities in the secondary school history classroom. It shows how history can be taught as an interpretation, and students can gain a deeper understanding of the constructive and interpretive nature of historical knowledge and interpretations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.511
GPT teacher head0.611
Teacher spread0.100 · 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 teacher head, not a consensus.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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