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Record W3097859007 · doi:10.1080/13603116.2020.1837265

‘The road less travelled’: grade-level acceleration in inclusive schools

2020· article· en· W3097859007 on OpenAlexaff
Lynn Dare, Elizabeth Nowicki

Bibliographic record

VenueInternational Journal of Inclusive Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychological interventionInclusion (mineral)Intervention (counseling)PsychologyAccelerationSpecial needsSpecial educationMathematics educationMedical educationPedagogySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Students with high academic ability have unique educational needs. Like all students with special needs, they require evidence-based interventions to develop their potential. One recommended intervention is educational acceleration; however, some educators express concerns about this intervention, often due to worries about potential social issues. Little is known about educational acceleration in inclusive schools, where social inclusion is fostered. In this phenomenological study, we describe the experiences of young people between 17 and 28 years of age, who undertook grade-level acceleration in inclusive schools. The purpose of our study was to describe the experiences of students who moved into classes with older classmates and explore their attitudes towards grade-based acceleration. We also gathered their insights on factors to consider when deciding on acceleration. Participants expressed positive opinions about access to accelerative interventions; they emphasised the need for personalised choices and thoughtful decision making. Important considerations include the school's social climate, supports available to students, and the educational needs of the individual student.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.426
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.086
GPT teacher head0.411
Teacher spread0.325 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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