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Record W2793437092 · doi:10.64546/jaasep.226

Preservice Teachers’ Attitudes Toward Inclusive Education Policy in the United States

2014· article· en· W2793437092 on OpenAlexaff
Paul M. Ajuwon, Effie Laman, John Christopher Earle

Bibliographic record

VenueJournal of the American Academy of Special Education Professionals · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsPsychologyMathematics educationSpecial educationPedagogyInclusion (mineral)Postsecondary educationState policyTeacher educationHigher educationPolitical scienceMedical educationPolicy analysisPublic administrationMedicineSocial psychology

Abstract

fetched live from OpenAlex

The attitudes of 224 preservice teachers from eight universities in the United States were measured to determine if participants’ sentiments, attitudes, and concerns about inclusion can be positively affected through a single course, i.e., using pre and post data gathered with one instrument. There were significant differences between a number of institutions’ pre and post attitudes, sentiments, and concerns that likely stem from variations in the curricula and timing of the individual courses. Key demographic variables appeared to significantly account for the wide range of responses in sentiments, attitudes and concerns in both the pre- and post-training surveys. The percent variance explained by each demographic variable indicates the most influential factors were the level of confidence in one’s ability to teach in an inclusive setting, the candidates’ level of interactions with persons with a disability, previous training related to working with persons with a disability, knowledge of legislation and policy regarding inclusion, and in their previous experience teaching students with disabilities. Legislation and policy can easily be taught in inclusive programs, but important factors relating to confidence and experience with persons with a disability require "real world", structured opportunities to promote inclusion.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.442
Teacher spread0.413 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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