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Record W4402718163 · doi:10.1017/s0008423924000131

Groups, Identity, and Redistributive Preferences in Canada

2024· article· en· W4402718163 on OpenAlexafffundabout
Sophie Borwein

Bibliographic record

VenueCanadian Journal of Political Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of Toronto
KeywordsIdentity (music)Redistribution (election)Political scienceLawPoliticsArtAesthetics

Abstract

fetched live from OpenAlex

Abstract Recent political developments in established democracies have renewed attention to the politics of identity. Some commentators have expressed concern that polities are fracturing along increasingly narrow social identity lines, in the process, losing their ability to build solidarity around shared commitments such as redistribution. This article takes stock of the strength of Canadian social identities and their consequences for redistributive preferences. It asks: first, which group memberships form the basis of Canadians’ perceptions of shared identity, and second, do these group memberships shape preferences for redistribution? This study answers these questions using two conjoint experiments that assess respondents’ perceptions of commonality and support for redistributing to hypothetical Canadians who vary on multiple dimensions of identity and need. Findings support that Canadians perceive greater shared identity with some of their groups (their social class) over others (their region or ascriptive identity), but that they overwhelmingly prioritize redistributing toward those who need it over those with whom they share group memberships.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.056
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0130.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.036
GPT teacher head0.338
Teacher spread0.303 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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