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Record W4366112856 · doi:10.7202/1098247ar

Pour une éducation populaire à la fiscalisation de la protection sociale

2023· article· fr· W4366112856 on OpenAlexaffvenueabout
Marie-Pierre Boucher

Bibliographic record

VenueRecherches sociographiques · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Dès la fin des années 1960, la protection sociale canadienne allait progressivement s’amalgamer au rapport d’impôt. Or cette tendance à la fiscalisation de la protection sociale semble encore mal connue des populations qu’elle vise. Dans une perspective d’analyse des politiques publiques « par et pour », nous avons élaboré un projet de recherche collaborative avec des intervenantes en placement de la main-d’oeuvre féminine. Mais celui-ci a confirmé les résistances à la compréhension de cette dynamique de fiscalisation. Dans cet article, nous allons présenter cette démarche de recherche et certains des outils mobilisés au cours de celle-ci. Nous allons ensuite remonter le fil de notre propre démarche de connaissances de la fiscalisation de la protection sociale à partir du projet d’allocation universelle, commencée il y a 25 ans. Ce faisant, nous souhaitons mettre en évidence comment ce projet qui suscite la mobilisation populaire pourrait conduire à alimenter une démarche d’éducation populaire sur la fiscalisation de la protection sociale.

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)0.006
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0070.008
Scholarly communication0.0070.004
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0260.002

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 head0.218
GPT teacher head0.428
Teacher spread0.210 · 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
GenreCommentary

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

Citations0
Published2023
Admission routes3
Has abstractyes

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