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Record W98113677 · doi:10.5206/eei.v18i3.7627

Identified Teacher Supports for Inclusive Practice

2008· article· en· W98113677 on OpenAlexaffvenue
Phyllis E. Horne, Vianne Timmons, Rosalyn Adamowycz

Bibliographic record

VenueExceptionality Education International · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsInclusion (mineral)CurriculumAcknowledgementClass (philosophy)Government (linguistics)PsychologyIncentivePedagogyMathematics educationClass sizeScale (ratio)Special educationMedical educationMedicineSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This study investigated inclusive practices in Prince Edward Island (PEI) elemen-tary schools in terms of the supports teachers consider as important for inclusion. Twenty teachers were randomly selected to complete a survey, and 5 teachers were randomly selected to participate in an interview about inclusion supports. The survey in this study adapted The School and the Education of All Students Scale. Participants identified and ranked several supports that they deemed im-portant for successful inclusion. The results indicated that elementary teachers in PEI consider certain supports as important when planning an inclusive class-room, such as class size, curriculum and planning time, training, and other incentives. In light of PEI’s continued transition in Special Education services, such results provided insight into specific recommendations. The identified teach-er supports necessitate acknowledgement and understanding by teachers, parents, school boards, government, and teacher-training programs to ensure inclusive practices are implemented effectively in the PEI school system.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.034
GPT teacher head0.431
Teacher spread0.397 · 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 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

Citations3
Published2008
Admission routes2
Has abstractyes

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