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Record W4318215343 · doi:10.1108/s1479-3547202313

Disability in the Time of Pandemic

2023· book· en· W4318215343 on OpenAlexaboutno aff
Allison C. Carey, Sara Green, Laura Mauldin

Bibliographic record

VenueResearch in social science and disability · 2023
Typebook
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Computer scienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

COVID-19 has once again illuminated the ways in which health risks and negative health outcomes are tied to economic and social inequalities. Disabled people rank among those most disadvantaged in terms of education, income, and social inclusion and this exacerbated their risk of negative pandemic-related outcomes. From the start, it was clear that disabled people would be disproportionately affected by the pandemic and this solidified as the pandemic unfolded. Disability in the Time of Pandemic is a timely exploration of emerging research into the implications of the COVID-19 pandemic for people with disabilities in their varied communities and across their complex identities. Using the insights, perspectives, and methods of a variety of disciplines including Anthropology, Disability Studies, Education, Physical and Rehabilitation Therapies, Public Health, Psychology, Sociology, and Women's and Gender Studies, authors explore the initial and ongoing effects of the global pandemic on people with disabilities in Canada, India, Poland, and the United States. The Research in Social Science and Disability series is essential reading for researchers and students across the social sciences interested in disability, social movements, activism, and identity.

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)0.031
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.015
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.340
GPT teacher head0.586
Teacher spread0.246 · 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.

Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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