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Record W4328051351 · doi:10.31542/muse.v6i1.2281

Inclusive Education

2023· article· en· W4328051351 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueMacEwan University Student eJournal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsMacEwan University
Fundersnot available
KeywordsUniversal Design for LearningInclusion (mineral)Gateway (web page)Order (exchange)Universal designSpecial needsLearning environmentPedagogyMathematics educationComputer scienceEngineering ethicsPsychologyEngineeringBusinessWorld Wide Web

Abstract

fetched live from OpenAlex

Schools and other learning environments have increasingly promoted equal educational opportunities among all students in recent years to create more inclusive learning environments. Inclusive education has impactful benefits on all students, not just those with diverse needs, as it is "the gateway to full participation in society" (United Nations, n.d, pg. 3). However, barriers to inclusion such as staff training and funding are necessary to consider in order to create and maintain meaningful, inclusive learning environments. In order to overcome these barriers to inclusive education, educators have been encouraged to integrate aspects of both Universal Design for Learning (UDL) and Differentiated Instruction (DI) to maintain the least restrictive environment while tailoring to individual student needs. The integration of UDL principles into classroom/learning environments promotes inclusion through supports for all students, including those with diverse needs. Educators can utilize strategies like the Three-Block Model of Universal Design for Learning (TBM) and ADAPT to aid them in the integration of UDL.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.364
Teacher spread0.343 · 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