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Record W2344468488 · doi:10.1111/hoeq.12181

Creating the “International Mind”: The League of Nations Attempts to Reform History Teaching, 1920–1939

2016· article· en· W2344468488 on OpenAlexaff
Ken Osborne

Bibliographic record

VenueHistory of Education Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLeagueRetrainingCurriculumPolitical scienceWorld historyPublic administrationLawSociologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

After the First World War, the League of Nations, through its International Committee on Intellectual Cooperation, attempted to reshape the teaching of history in its member states. The League's supporters realized that its long-term success depended in part on supportive public opinion and that this, in turn, had implications for education. Aware of the strength of national loyalties, the League sought not to abolish the teaching of national history but to suffuse it with the spirit of the “international mind.” To this end, the League promoted revision of history textbooks and curricula, retraining of teachers, and rethinking of teaching methods. National governments responded by including some study of the League in history curricula but ignored the League's broader plans. Nonetheless, the League's attempt to internationalize the teaching of history opened up a debate that continues today as schools seek to strike a balance between claims of national and global history.

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)0.008
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 score0.048
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.015
Scholarly communication0.0080.004
Open science0.0010.006
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.358
Teacher spread0.279 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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