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From the Past to the Future: Changing Agendas in Teacher Education between the 19th and the 21st Century

2012· article· en· W1568255972 on OpenAlexvenueno aff
Anne Rohstock, Daniel Tröhler

Bibliographic record

VenueEncounters in Theory and History of Education · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Education Studies Worldwide
Canadian institutionsnot available
Fundersnot available
KeywordsVisionInternationalizationContext (archaeology)Teacher educationNationalismPolitical sciencePedagogyStandardizationSociologyHistoryPoliticsLaw

Abstract

fetched live from OpenAlex

The educational turn of the late eighteenth century, nation building of the nineteenth century, and efforts to promote global unity after the two World Wars did not only have effects on educational organizations, policies, and materials, but also on the manner with which the major actors in the world of education—namely, teachers – were trained. The different ideals and agendas in teacher training reflected the major cultural concerns of each era: in the nineteenth century, this was national uniqueness and supremacy, which, in the post war period, gave way to internationalization and global standardization. These visions were associated with the emergence of particular academic subfields and heavily shaped pedagogical ideals. In the era of nation building, the history of education dominated teacher education. In the context of the Cold War teacher training was aligned with a new internationalist and scientific paradigm. The following chapter discusses these two agendas in teacher education. In the first section we will reconstruct the rise of the history of education as a major subject in nationalist and religiously inspired teacher education in Germany and France. In the second section we will show how this leitmotif in the Cold War era was supplanted by a “cognitive turn” in the training of professional educators.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.295
Teacher spread0.282 · 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.

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
Published2012
Admission routes1
Has abstractyes

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