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Record W3200126688 · doi:10.7202/1082924ar

Barriers to Knowing and Being Known: Constructions of (In)competence in Research

2021· article· en· W3200126688 on OpenAlexvenueno aff
Casey Woodfield, Justin E. Freedman

Bibliographic record

VenuePhilosophical Inquiry in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)PsychologyAugmentative and alternative communicationIntellectual disabilityEducational researchAgency (philosophy)PedagogyKnowledge productionDiversity (politics)Social psychologySociologyEpistemologyKnowledge managementSocial scienceComputer science

Abstract

fetched live from OpenAlex

In this paper, we examine the barriers to, and possibilities of, recognizing individuals labelled intellectually disabled as producers and contributors to knowledge about their experiences. Through engaging perspectives within the fields of philosophy of education and disability studies, we examine contrasting research about the use of facilitated communication, an augmentative and alternative communication technique for teaching people with disabilities to communicate through pointing, or typing with support provided by a communication partner. We examine how researchers impose demands for the scientific validation of facilitated communication and use such demands to discredit autistic people identified with intellectual disabilities in their attempts to be recognized as knowers and producers of knowledge. Our analysis calls into question whether self-imposed limitations on contemporary knowledge production render educational research (in)capable of accepting forms of evidence that will facilitate the agency of those labelled or regarded as intellectually disabled and (in)capable of providing consumers of educational research access to knowledge that reflects the wide range of communicative, neurocognitive , and intellectual diversity in schools and communities.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.191
GPT teacher head0.476
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations3
Published2021
Admission routes1
Has abstractyes

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