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(Re)framing identity claims: <scp>E</scp>uropean and state institutions as opportunity windows for group reinforcement

2012· article· en· W1959331599 on OpenAlexaff
Magdalena Dembińska

Bibliographic record

VenueNations and Nationalism · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCognitive reframingFraming (construction)Ethnic groupPoliticsCollective identityIdentity (music)SociologyState (computer science)Political economyPolitical scienceGender studiesLawSocial psychologyEngineeringPsychologyAestheticsComputer science

Abstract

fetched live from OpenAlex

Abstract How do we account for the reinforcement of identity particularisms despite transnational integration? This paper addresses the question by comparing two ethnolinguistic groups, Silesians and Kashubs in Poland. It is argued that in order to obtain state protection and tools to develop and survive, ethnic entrepreneurs adjust to institutions and discourses. Census politics, state laws' elaboration, transnational institutions represent openings to which groups adjust by reframing identity claims. In doing so, they re‐imagine and reinforce their communities. Following Rogers Brubaker, group‐making is presented as an eventful process where ethnic elites invest identity categories with groupness by taking advantage of opportunity windows at hand. Further, tracing changing political opportunities, strategic adjustments and groups' boomerang effect bid, the paper embeds identity groups within the social movement literature.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.021
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.070
GPT teacher head0.360
Teacher spread0.289 · 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 designQualitative
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

Citations13
Published2012
Admission routes1
Has abstractyes

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