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Record W2612762654 · doi:10.18357/jcs.v39i3.15233

Voices From the Field: Full-day Kindergarten Teams in Ontario Share Their Wisdom

2015· article· en· W2612762654 on OpenAlexaffvenueabout
Monica McGlynn-Stewart, Kimberly Bezaire

Bibliographic record

VenueJournal of Childhood Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsSalaryCertificationEarly childhoodTeam teachingPedagogyProfessional developmentEarly childhood educationPsychologyMedical educationField (mathematics)SociologyTeaching methodMedicinePolitical scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Canadian educators and policy makers are in the midst of implementing an innovative approach to education for 4- and 5-year olds. This study examined the perspectives of the teaching teams who are making this new full-day school-based model work: registered early childhood educators and Ontario certified teachers. These two groups have different professional backgrounds, status, salary, and professional development opportunities. Survey and interview results highlighted the complexity of the new teaching model and identified conditions that support interprofessional team building and effective team teaching in school settings. Themes arising from the data include the importance of relationship building, rethinking practice, sharing knowledge/specialization, and reestablishing roles. Implications and recommendations for teaching teams and system administrators are provided.

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)0.007
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0430.018
Scholarly communication0.0120.005
Open science0.0040.010
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.075
GPT teacher head0.363
Teacher spread0.288 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

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