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Record W2906552059 · doi:10.32920/ryerson.14653044.v1

Early Childhood Educators' Perception of their Training and Support Needs for Inclusive Education

2021· preprint· en· W2906552059 on OpenAlexaffabout
Evelina Siwik

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentGeorge Brown CollegeYork University
Fundersnot available
KeywordsInclusion (mineral)Thematic analysisPerceptionTriangulationPsychologyMeaning (existential)Medical educationQualitative researchPedagogyEarly childhood educationInterpretation (philosophy)SociologyMedicineSocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Inclusion literature is multifaceted; demonstrated by variation in meaning and interpretation of the concept. Within the body of literature on inclusion, educators' perceive training and support needs as key barriers to practicing inclusive education. The current study explores the perceptions of twenty early childhood educators across Toronto, Ontario about their own training and support needs for enacting inclusive education. A qualitative interview method, and triangulation with two questionnaires were used to collect data. The social model of disability was the theoretical framework that guided the research project. Major themes came from the topics identified in the literature and elicited through interview questions. Several subthemes also emerged during data analysis. Results of the thematic analysis suggest that opportunities for ongoing training and more access to resources are sought by educators and might increase confidence in their ability to include children with disabilities in their programs. Recommendations for future research and practice are discussed.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.286
Threshold uncertainty score0.569

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.372
Teacher spread0.331 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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