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Record W2235837652 · doi:10.5296/ije.v7i4.8260

An Interactionist Approach to Learning Disabilities

2015· article· en· W2235837652 on OpenAlexaff
Hilary Scruton, John K. McNamara

Bibliographic record

VenueInternational Journal of Education · 2015
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsBrock University
Fundersnot available
KeywordsLearning disabilityPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Traditional approaches to understanding learning disabilities date back to the early twentieth century and are based primarily on medical models. Several useful strategies and techniques emerged from these early models and still influence today’s classrooms. However, there are also disadvantages to traditional approaches in that the models place much of the burden of the disability on the individual. Post-modern and strength-based perspectives on learning disabilities have attempted to account for the drawbacks of traditional models and have re-framed learning disabilities in broader social and cultural contexts. The current paper reviews these three perspectives and offers an alternative approach that attempts to bridge the modern and post-modern perspectives on learning disabilities. The interactionist approached offered in this paper calls for a processural or multi-faceted conception of learning disabilities. Interactionism encourages educators and students with and without learning disabilities to engage differences in ways that explore possibilities for productive and positive learning from each other.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0060.030
Scholarly communication0.0080.006
Open science0.0020.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.050
GPT teacher head0.416
Teacher spread0.366 · 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 designNot applicable
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

Citations4
Published2015
Admission routes1
Has abstractyes

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