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Record W2255445672 · doi:10.1177/0022487115624490

A Five-Country Survey on Ethics Education in Preservice Teaching Programs

2016· article· en· W2255445672 on OpenAlexafffundabout
Bruce Maxwell, Audrée-Anne Tremblay-Laprise, Marianne Filion, Helen Boon, Caroline Daly, Ruth Heilbronn, Myrthe Lenselink, Sue Walters

Bibliographic record

VenueJournal of Teacher Education · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsMcGill UniversityUniversité du Québec à Trois-Rivières
FundersFonds de Recherche du Québec-Société et Culture
KeywordsCurriculumTeacher educationPedagogyProfessional ethicsMeaning (existential)SociologyHigher educationPolitical scienceFace (sociological concept)PsychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Despite a broad consensus on the ethical dimensions of the teaching profession, and long-standing efforts to align teacher education with wider trends in professional education, little is known about how teacher candidates are being prepared to face the ethical challenges of contemporary teaching. This article presents the results of an international survey on ethics content and curriculum in initial teacher education (ITE). Involving five Organisation for Economic Co-Operation and Development (OECD) countries—the United States, England, Canada, Australia, and the Netherlands—the study’s findings shed light on teacher educators’ perspectives on the contribution of ethics content to the education of future teachers and provide a snapshot of how well existing programs line up with their aspirations. The results showed that 24% of the ITE programs surveyed contain at least one mandatory stand-alone ethics course. The meaning of the results vis-à-vis opportunities for expanding ethics education in preservice teaching programs is also discussed.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationalhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.025
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.235
GPT teacher head0.491
Teacher spread0.256 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations75
Published2016
Admission routes3
Has abstractyes

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