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Record W3208766958 · doi:10.18357/jcs463202119970

Pandemic Effects: Ableism, Exclusion, and Procedural Bias

2021· article· en· W3208766958 on OpenAlexaffvenueabout
Kathryn Underwood, Tricia van Rhijn, Alice-Simone Balter, Laura Feltham, Patty Douglas, Gillian Parekh, Breanna Lawrence

Bibliographic record

VenueJournal of Childhood Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsYork UniversityBrandon UniversityUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsAbleismEarly childhoodPandemicEarly childhood educationSociologySocial exclusionLongitudinal studyCoronavirus disease 2019 (COVID-19)PsychologyDevelopmental psychologyEconomic growthPolitical scienceGender studiesMedicineEconomics

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has changed social organizations and altered children’s worlds. As part of an ongoing longitudinal study of the institutional organization of disabled children’s lives, since March 2020 we have conducted interviews with families in rural and urban communities across Canada (65 families at the time of writing). The narrow focus of governments on the economy, childcare, and schooling does not reflect the scope of experiences of families and disabled children. We describe emerging findings about what the effects of the pandemic closures demonstrate about the social valuing of childhood, disability, and diverse family lives in early childhood education and care. Our research makes the case that ableism, exclusion, and procedural bias are the products of cumulative experiences across institutional sites and that it is critical we understand disabled childhoods more broadly if we are to return to more inclusive early childhood education and care.

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)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.034
Scholarly communication0.0050.005
Open science0.0010.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.358
Teacher spread0.299 · 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 designQualitative
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

Citations11
Published2021
Admission routes3
Has abstractyes

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