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Record W2133113961 · doi:10.26522/brocked.v20i1.145

Reflecting From the Margins of Education Faculties: Refiguring the Humanist, and Finding a Space for Story in History

2010· article· en· W2133113961 on OpenAlexaffvenue
Theodore Michael Christou

Bibliographic record

VenueBrock Education Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNarrativeHumanismMemoirSociologyDisciplineHistory of educationConflationAestheticsSubject (documents)Space (punctuation)PedagogyEpistemologyLiteratureSocial scienceLawPhilosophyArtPolitical science

Abstract

fetched live from OpenAlex

Notwithstanding their traditional characterization as a foundations subject, history of education courses are marginal in pre-service teacher education. This marginalization is framed here in light of a broader concern for the discipline’s turn away from the humanities. History of education’s fundamental purpose, it is argued, lies in the exploration of what it means to be human, and how education has historically been shaped by our values, authority, contexts, and norms. Using stories drawn from literature and memoirs in the teaching of educational history is one means of exploring intersections of education with human cultures and societies across historical contexts. History is etymologically linked with story telling, and both history and literature share narrative features; the two should not be conflated, however, due to distinctive disciplinary features of history, such as the requirement that any claims to truth require what John Dewey referred to as warranted assertability.Keywords: history of education, teacher education,educational foundations, humanities, literature, historical mindedness, John Dewey.

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)0.004
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 score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0150.061
Scholarly communication0.0120.017
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.146
GPT teacher head0.420
Teacher spread0.275 · 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

Citations4
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueBrock Education JournalSame topicEducator Training and Historical PedagogyFrench-language works237,207