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National Attachment—Cohesive, Divisive, or Both?

2019· book-chapter· en· W4230413046 on OpenAlexaboutno aff
Gina Gustavsson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsNational identitySolidarityPrideChauvinismSocial psychologyIdeologyEgalitarianismPolitical scienceSympathyContext (archaeology)SociologyPsychologyGeographyLawPolitics

Abstract

fetched live from OpenAlex

Abstract A large number of empirical studies of in-group sympathy and helpfulness suggest that the sheer strength of a person’s national attachment should increase her solidarity with her co-nationals. This relationship is likely to be independent of how she conceives the content of that identity and the extent to which she believes her co-nationals share this commitment. The direction of this link, moreover, might look different depending on whether the dimension of national attachment at hand is national identity, national pride, or national chauvinism. The chapter utilizes survey data from the Netherlands (LISS), arguably a more relevant context for liberal nationalists than the more typically studied cases of the USA and Canada. The Dutch demonstrate significant relationships between national attachment and support for egalitarianism, even when controlling for ideology. National identity turns out to be consistently related to more willingness to share resources with our co-nationals, whilst national pride shows a negative link to redistributive solidarity.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.050
GPT teacher head0.328
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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2019
Admission routes1
Has abstractyes

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