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Record W3034249043 · doi:10.29173/pandpr29400

Professional Ethics as Experienced by Student Teachers: A Neoliberal View

2020· article· en· W3034249043 on OpenAlexvenueno aff
Marita Cronqvist

Bibliographic record

VenuePhenomenology & Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsInternshipPedagogyProfessional ethicsAccountabilityLived experienceSociologyProfessional developmentPsychologyEngineering ethicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Student teachers’ experiences of professional ethics, as lived practice, need to be visualized and verbalized to support their ability to develop an ethical practice. The aim of this article is to discuss the lived experiences of professional ethics from beginning teachers’ internship, based on a phenomenological study. Some of the essential meanings are interpreted in relation to the tension between responsibility and accountability that is emerging from neoliberal influences in teacher education. Inspired by Reflective Life World Research (RLR), interviews were conducted with student teachers specializing in preschool and elementary school. The empirical data was analyzed in order to determine the meanings that constitute the lived experience of professional ethics for early career teachers. By identifying the implications of professional ethics in neoliberal times, teacher educators can more easily observe and communicate the manifestations this has for teaching. Discussions and observations of professional ethics can stimulate student teachers’ learning as part of teacher education discourse.

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)0.005
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.030
Scholarly communication0.0150.008
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.477
Teacher spread0.389 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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