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Record W1790054086

The Journey of two Physical Education and Health Teachers in Learning to Teach Personal and Social Responsibility

2015· article· en· W1790054086 on OpenAlexaffabout
Sylvie Beaudoın, Jean-Pierre Brunelle, Carlo Spallanzanı

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPhysical educationSocial responsibilityCurriculumPedagogyPsychologyProfessional developmentPersonal developmentMoral responsibilityAction (physics)Mathematics educationMedical educationPublic relationsMedicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Responsibility development is a key feature of the Province of Quebec’s elementary school curriculum. However, it does not provide teachers with clear direction on how to teach personal and social responsibility. A nine-month action research project was conducted during the 2008-2009 school-year with two physical education and health teachers willing to implement the Teaching Personal and Social Responsibility (TPSR, Hellison, 1995; 2003; 2011) model in their respective settings. The purpose of this study is to describe each teacher’s journey in learning how to teach personal and social responsibility. Data sources included participant observations, semi-structured interviews and post-teaching self-reflections. Core categories qualifying PEH teachers’ professional development processes were generated from data analysis and revealed two different profiles. The findings are discussed through Martinez’s (1993) professional development model.

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)0.006
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 score0.434
Threshold uncertainty score0.862

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.010
Scholarly communication0.0060.003
Open science0.0020.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.200
GPT teacher head0.539
Teacher spread0.339 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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