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Record W4235120397 · doi:10.32920/ryerson.14661423.v1

Is A Partnership Possible? A Comparison Of Elementary Teachers' And Early Childhood Educators' Perceptions Of Disability And Inclusion

2021· preprint· en· W4235120397 on OpenAlexaboutno aff
Colleen Thornton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)PerceptionAttributionGrounded theoryEarly childhoodQualitative researchPedagogyPsychologyEarly childhood educationGeneral partnershipDevelopmental psychologySociologySocial psychologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

Education literature presents diverse perceptions of disability and inclusion by educators and scholars. Past research has shown that educators' beliefs influence their practice. The Full-Day Early Learning Program will commence in Ontario schools in September 2010, which will involve teacher and early childhood educator teaching teams. This qualitative research study presents two elementary teachers' and two early childhood educators' perceptions of disability and inclusion. Using a grounded theory strategy of inquiry, two interviews were conducted with each participant. A poststructural lens was used to analyze and interpret data. Key findings show distinct understandings of disability and inclusion between the two educator groups, which relate to their pedagogical beliefs and views of the purpose of education. This study draws on attribution theory and a social relational model of disability to explore the implications of participants' perceptions for children's education. Recommendations for future research and practice and identified and briefly explored.

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)0.010
metaresearch head score (Gemma)0.020
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0070.005
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.376
Teacher spread0.346 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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Same topicCollaborative Teaching and InclusionFrench-language works237,207