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Record W1252692582 · doi:10.7202/1029423ar

Ontario Kindergarten Teachers’ Social Media Discussions About Full Day Kindergarten

2015· article· en· W1252692582 on OpenAlexaffvenueabout
Meghan Lynch

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNetnographyCurriculumQualitative researchExploratory researchPedagogyPsychologySocial mediaClass (philosophy)Mathematics educationEarly childhood educationSociologyMedical educationPolitical scienceMedicineSocial scienceComputer science

Abstract

fetched live from OpenAlex

This exploratory netnographic study describes how a sample of Ontario kindergarten teachers perceives the new Ontario Full Day Kindergarten (FDK) curriculum. Discussions from teacher message boards, the comment sections of online news articles, and interviews with kindergarten teachers were analyzed and coded using a qualitative approach. Analysis revealed three major themes: 1) Class size concerns, 2) Team teaching concerns, and 3) Play-based curriculum concerns. Results are in broad agreement with those reported in existing research into Ontario’s FDK initiative. Findings highlight the need for further research with educators involved in Ontario’s FDK and also contribute to the burgeoning field of netnography research. Suggestions for future research and practice are included.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.259
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.339
GPT teacher head0.412
Teacher spread0.072 · 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

Citations24
Published2015
Admission routes3
Has abstractyes

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