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Record W4312599540 · doi:10.7202/1090979ar

Inégalités et COVID-19 : impacts de la crise sanitaire sur les opinions à l’égard des personnes assistées sociales et leur représentation médiatique au Québec1

2022· article· fr· W4312599540 on OpenAlexaffvenueabout
Normand Landry, Alexandre Blanchet, Olivier Santerre, Marie-Josée Dupuis, Sylvain Rocheleau

Bibliographic record

VenueLien social et Politiques · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité de SherbrookeUniversité TÉLUQ
Fundersnot available
KeywordsHumanitiesPolitical scienceCoronavirus disease 2019 (COVID-19)ArtMedicine

Abstract

fetched live from OpenAlex

Cet article présente les résultats d’analyse d’un sondage effectué auprès de 2060 répondants en juin 2020 quant à leurs opinions à l’égard des personnes assistées sociales au Québec. Il fait état des impacts de la crise sanitaire sur ces opinions et il offre une analyse de la couverture médiatique de l’assistance sociale effectuée entre le 23 mars et le 25 juin 2020. Cette période correspond au premier confinement vécu au Québec en raison de la COVID-19. Les conclusions mettent en lumière une congruence entre des opinions durablement négatives à l’égard des personnes assistées sociales, un faible niveau d’acceptabilité sociale des aides particulières qui pourraient leur être versées en période de crise sanitaire, une marginalisation médiatique des thèmes et des enjeux associés à l’assistance sociale en contexte de crise sanitaire, et l’absence de mesures mises en place par le gouvernement du Québec afin d’atténuer ses impacts pour les personnes assistées sociales.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.136
GPT teacher head0.446
Teacher spread0.309 · 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 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

Citations1
Published2022
Admission routes3
Has abstractyes

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