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Record W2550865570 · doi:10.5206/eei.v28i1.7756

Four Secondary Teachers’ Perspectives on Enhancing the Inclusion of Exceptional Students

2018· article· en· W2550865570 on OpenAlexaffvenueabout
Kyle Robinson

Bibliographic record

VenueExceptionality Education International · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsQueen's University
Fundersnot available
KeywordsInclusion (mineral)PsychologyMathematics educationReferralPedagogySpecial educationSemi-structured interviewMedical educationQualitative researchSociologyMedicineSocial psychologyNursing

Abstract

fetched live from OpenAlex

In Canada little research has been conducted on inclusive education practices in secondary schools. The purpose of this study is to report, for a diverse group of four secondary school teachers in a single school board in southeastern Ontario, their descriptions of facilitating the inclusion of exceptional students in general classrooms. The four teachers were recruited using an email referral method. Each of them participated in a semi-structured interview about their educational roles and role expectations, and about their reported instructional methods for inclusion. Seven categories emerged from the analyzed data, and these were clustered to form three themes: Structures and People, Meeting Everyone’s Needs, and Knowing Your Students. The findings suggest that the participants in this study were facilitating inclusion of exceptional students in regular classrooms by considering how the students’ functional needs impact their learning; most considered the functional learning and assessment needs of all students, not just exceptional students.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.469
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0280.008
Scholarly communication0.0050.001
Open science0.0010.006
Research integrity0.0010.003
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 head0.027
GPT teacher head0.401
Teacher spread0.374 · 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
Published2018
Admission routes3
Has abstractyes

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