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Record W4224942936 · doi:10.1080/10400435.2022.2058315

Language matters! The long-standing debate between identity-first language and person first language

2022· editorial· en· W4224942936 on OpenAlexaff
Krista L. Best, W. Ben Mortenson, Zach Lauzière-Fitzgerald, Emma Smith

Bibliographic record

VenueAssistive Technology · 2022
Typeeditorial
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of British ColumbiaCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité LavalVancouver Coastal Health Research InstituteVancouver Coastal HealthCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsIdentity (music)LinguisticsLanguage barrierOn LanguageSociologyPsychologyComputer scienceArtAesthetics

Abstract

fetched live from OpenAlex

There has been a long-standing debate about person-first versus identify-first language in the study of disability. In other words, whether it is more acceptable to describe someone as a “disabled ...

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)0.014
metaresearch head score (Gemma)0.043
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.036
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.043
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0060.004
Science and technology studies0.0090.009
Scholarly communication0.0200.012
Open science0.0050.003
Research integrity0.0360.043
Insufficient payload (model declined to judge)0.0130.009

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 head0.291
Teacher spread0.268 · 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
GenreEditorial

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

Citations83
Published2022
Admission routes1
Has abstractno

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