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Record W2505708043 · doi:10.1080/03057240.2016.1204271

Professional ethics education for future teachers: A narrative review of the scholarly writings

2016· review· en· W2505708043 on OpenAlexafffund
Bruce Maxwell, Marina Schwimmer

Bibliographic record

VenueJournal of Moral Education · 2016
Typereview
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsMcGill UniversityUniversité du Québec à Trois-Rivières
FundersFonds de Recherche du Québec-Société et Culture
KeywordsViewpointsCurriculumNarrativeEngineering ethicsInformation ethicsSociologyPedagogyApplied ethicsMeta-ethicsTeacher educationNormative ethicsPolitical scienceLawPhilosophyEngineering

Abstract

fetched live from OpenAlex

This article provides a narrative review of the scholarly writings on professional ethics education for future teachers. Against the background of a widespread belief among scholars working in this area that longstanding and sustained research and reflection on the ethics of teaching have had little impact on the teacher education curriculum, the article takes stock of the field by synthesizing viewpoints on key aspects of teaching ethics to teacher candidates—the role ethics plays in teacher education, the primary objectives of ethics education for teachers, recommended teaching and learning strategies, and challenges to introducing ethics curriculum—and maps out how opinions on these matters have evolved over the three decades since the initial publication of Strike and Soltis’ seminal book, The Ethics of Teaching. In light of the review’s results, the article identifies critical deficits in this literature and proposes a set of recommendations for future inquiry.

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)0.016
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.009
Science and technology studies0.0030.003
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.384
GPT teacher head0.567
Teacher spread0.183 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations99
Published2016
Admission routes2
Has abstractyes

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