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Record W3128214056 · doi:10.1163/25888803-bja10006

The Tripartite Intervention: Breaking Down Attitudinal Barriers in Education

2021· article· en· W3128214056 on OpenAlexaff
John Freer

Bibliographic record

VenueJournal of Disability Studies in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsUniversity of WindsorSt. Clair College
Fundersnot available
KeywordsIntervention (counseling)FriendshipPsychological interventionPsychologyInclusion (mineral)Cognitive disabilitiesCognitionFace (sociological concept)Medical educationPedagogySocial psychologyMedicineSociology

Abstract

fetched live from OpenAlex

Abstract Students with disabilities face attitudinal barriers to social inclusion. Poor attitudes toward disability can impact friendship development between students with and without disabilities and result in social exclusion. Fortunately, a body of literature exists, which suggests that educational interventions can help to enhance students’ attitudes toward disability. Such interventions, however, vary in both their approach to teaching students about disability experiences and their effectiveness. This paper presents the Tripartite Intervention, a 12-lesson intervention created for junior-level elementary students that targets cognitive, behavioural, and affective dimensions of attitude. This paper provides a high-level overview of the intervention lessons, as well as, offers readers some practical considerations for implementation. Those interested in utilizing the Tripartite Intervention for educational or research purposes are encouraged to contact the author for intervention materials and training opportunities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.594
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.453
Teacher spread0.410 · 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 teacher head, 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

Citations7
Published2021
Admission routes1
Has abstractyes

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