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Record W2307650400 · doi:10.1177/0149206316638160

To Say or Not to Say: Different Strategies of Acknowledging a Visible Disability

2016· article· en· W2307650400 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.

Bibliographic record

VenueJournal of Management · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAffect (linguistics)PsychologyPerceptionSocial psychologyCompetence (human resources)Stereotype (UML)Interpersonal communication

Abstract

fetched live from OpenAlex

Individuals with visible disabilities can acknowledge their disabilities in different ways, which may differ in effectiveness. Across four studies, we investigate whether individuals with visible disabilities engage in different acknowledgment strategies (claiming, downplaying) and how and why these different strategies affect evaluations from others. Specifically, we draw from the Stereotype Content Model and Stereotype-Fit Theory to articulate a process whereby claiming and downplaying differentially affect others’ perceptions of competence and warmth, which subsequently affect overall evaluations of the individual with a disability. We found that individuals with visible disabilities intentionally manage others’ impressions by engaging in claiming and downplaying. Claiming strategies (relative to downplaying or not acknowledging) resulted in higher evaluations because they activated perceptions of competence and warmth and the benefits of claiming were stronger for jobs higher in interpersonal demands. We discuss the implications of these results for individuals with disabilities and for organizations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.351
Teacher spread0.250 · 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