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Record W2099253260 · doi:10.1017/s1755773913000088

Testing the national identity argument

2013· article· en· W2099253260 on OpenAlexaboutno aff
David Miller, Sundas Ali

Bibliographic record

VenueEuropean Political Science Review · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsNational identityPatriotismArgument (complex analysis)PrideIdentity (music)Political scienceEconomic JusticeSocial identity theoryState (computer science)Welfare stateSociologyDemocracyPolitical economyGender studiesSocial psychologyLawPoliticsSocial groupSocial sciencePsychology

Abstract

fetched live from OpenAlex

The national identity argument holds that a shared national identity is necessary to motivate citizens in democratic societies to pursue a number of goals, especially social justice. We review the empirical evidence for and against this claim, looking particularly at how national identities have been measured. We distinguish between studies that aim to compare the relative strength of identities cross-nationally and those that look at individual differences within one nation. We separate four dimensions of national identity: national attachment, national pride, critical vs. uncritical patriotism, and civic vs. cultural conceptions of identity. These are only weakly correlated with each other, and impact differently on support for social justice and the welfare state. Using case studies from the United States, Canada, and the United Kingdom, we suggest that the relationship between national identity and social justice varies between societies, and that a key factor is finding an appropriate balance between the strength of such identities and their inclusiveness.

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)0.046
metaresearch head score (Gemma)0.133
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.133
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0030.011
Scholarly communication0.0040.014
Open science0.0030.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0110.001

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 head0.166
GPT teacher head0.424
Teacher spread0.258 · 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

Citations236
Published2013
Admission routes1
Has abstractyes

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