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Record W2915135885 · doi:10.18357/jcs.v44i1.18775

Exploring early childhood educators’ notions about professionalism in Prince Edward Island

2019· article· en· W2915135885 on OpenAlexvenueaboutno aff
Alaina Roach O’Keefe, Sonya Hooper, Brittany A. E. Jakubiec

Bibliographic record

VenueJournal of Childhood Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEarly childhoodEarly childhood educationSociocultural evolutionProfessional learning communityPedagogyThematic analysisCurriculumProfessional developmentVariety (cybernetics)SociologyQualitative researchPsychologySocial scienceDevelopmental psychologyAnthropology

Abstract

fetched live from OpenAlex

Despite policy changes in a growing number of countries to increase the quality of early years education through the introduction of national curricular frameworks, conceptualizations of early childhood professionals remain distinctly variegated. Early learning curriculum frameworks have become embedded into the 21st-century early learning movement, creating a shift in professional deliverables and system expectations. This study explores how early childhood educators (ECEs) in Prince Edward Island (PEI) understand the concept of professionalism in their everyday practice. The researchers used qualitative methodology and a variety of methods, including workshops, interviews, and field notes, to gain insight into how ECEs understand professionalism. The data was analyzed through thematic analysis and understood through the lens of sociocultural theories of learning that embrace communities of practice as a positive way to promote professional learning. Primary findings explore (1) how ECEs understand professionalism in PEI, (2) positive and negative impacts on their understanding of professionalism in their daily practice, and (3) professional development opportunities that impact professionalism in the early childhood field.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.348
Teacher spread0.283 · 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 teacher head, 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

Citations8
Published2019
Admission routes2
Has abstractyes

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