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Record W4223599235 · doi:10.1080/13540602.2022.2062716

The coalition model for professional development

2022· article· en· W4223599235 on OpenAlexaffabout
Alexandra Youmans, Lorraine Godden

Bibliographic record

VenueTeachers and Teaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsCarleton UniversityQueen's University
Fundersnot available
KeywordsProfessional developmentQualitative researchWork (physics)SociologyPedagogyField (mathematics)Public relationsMedical educationPsychologyPolitical scienceEngineeringMedicineSocial science

Abstract

fetched live from OpenAlex

The Coalition Model for Professional Development (CMfPD) was created to develop capacity in a network of adult and continuing education (A&CE) staff from eight district school boards in the eastern Ontario region in Canada. The CMfPD had three main components: 1) a collaborative structure, 2) continuous learning and 3) a culture of care. Interviews were conducted with twenty-two network members to explore their experiences with the CMfPD. Qualitative analysis of interview data revealed positive professional development (PD) experiences associated with model components. In addition, network members reported professional growth as a result of the CMfPD, with respect to increasing their capacity, promoting collaboration with their colleagues and preparing them to enact change in the field of A&CE. The importance of this PD model is discussed in relation to how its components helped mobilise the collective work of a newly formed education coalition dedicated to system-wide change.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0080.019
Scholarly communication0.0110.007
Open science0.0020.009
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0160.004

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 head0.122
GPT teacher head0.439
Teacher spread0.317 · 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 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

Citations7
Published2022
Admission routes2
Has abstractyes

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