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Record W3012475823 · doi:10.1017/s0007123419000589

Material Interests, Identity and Linked Fate in Three Countries

2020· article· en· W3012475823 on OpenAlexaff
Michael Donnelly

Bibliographic record

VenueBritish Journal of Political Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBridge (graph theory)Identity (music)Social psychologyPoliticsEthnic groupSocial identity theoryCognitionCollective identitySocial identity approachPsychologyObservational studyReligious identitySocial groupSociologyPositive economicsPolitical scienceEconomicsLawAesthetics

Abstract

fetched live from OpenAlex

Abstract This article examines the theoretical connections between identity and linked fate, extending the latter concept across three countries and four types of (potential) identity groups. This belief, that what happens to one's ethnic group, religious group, region, or class shapes one's own life chances, is an understudied middle ground between ideational and material drivers of political attitudes. The study uses experimental and observational analyses to show that the strength of individuals' beliefs in linked fate and that belief's consequences vary in systematic and predictable ways. From the very material effect of labor market uncertainty to the highly ideational effect of regional identity, linked fate is a cognitive bridge between two very different kinds of social–psychological experiences that can (and should) be applied across a wide range of countries and groups.

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)0.004
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.325
Teacher spread0.278 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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