MétaCan
Menu
Back to cohort
Record W4249730412 · doi:10.22215/rera.v7i2.219

Vicarious Evaluation: How European Integration Changes National Identities

2012· article· en· W4249730412 on OpenAlexafffundvenue
Philip Giurlando

Bibliographic record

VenueReview of European and Russian Affairs · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaEuropean CommissionPrinceton UniversityYale University
KeywordsEliteEuropean unionPerceptionPower (physics)HierarchyCompetition (biology)Political scienceNational powerIdentity (music)Development economicsDemographic economicsEconomicsPsychologyInternational tradePoliticsLaw

Abstract

fetched live from OpenAlex

This paper explores how European integration impacts the national identities of Member States. Identity is an amorphous concept, and so this paper focuses on one dimension of it: the perception of the relative status of the nation that nationalized individuals possess. Perceptions of relative national status flow from the fact that the international system is characterized by hierarchy, competition, and concerns for relative gains and losses. A key motivation for the foreign policies of lower status nations is equality with higher status ones, and for the former, European integration is often perceived in equalizing terms. But, this perception of Europe as equalizer often does not correspond with objectively unequal power relations in Europe. This paper focuses on why, among nationalized individuals, perceptions of power differentials change, even though objectively the unequal inter-state power relations may remain unchanged. The case study is Italy entering the Economic and Monetary Union of the European Union in 1999, which was perceived by many Italians in equalizing terms, even though the unequal power relations between Italy and Europe's elite countries remained objectively the same.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.929
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.330
Teacher spread0.280 · 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 teacher head, 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

Citations0
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueReview of European and Russian AffairsSame topicEuropean Union Policy and GovernanceFrench-language works237,207