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Record W2499804882 · doi:10.1057/9781403980496_1

Introduction: In Defense of Identity

2004· book-chapter· en· W2499804882 on OpenAlexaff
Patricia Goff, Kevin C. Dunn

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2004
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsIdentity (music)Dominance (genetics)Identity politicsLiberalismPoliticsSociologyRealismEpistemologyLexiconInternational relationsSocial identity theoryPolitical scienceSocial scienceAestheticsLawPhilosophySocial groupLinguistics

Abstract

fetched live from OpenAlex

Identity is back. The concept of identity has made a remarkable comeback in the social sciences and humanities. In International Relations (IR), many turned to identity-based analysis when the end of the Cold War disrupted the intellectual dominance of (neo)realism and (neo)liberalism. While it would be inaccurate to say that identity suddenly (re)emerged as a factor in world politics, as an analytical category it has only recently found its way into the conventional lexicon of IR theorists. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.003
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 score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0060.009
Open science0.0010.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0430.019

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 head0.020
GPT teacher head0.286
Teacher spread0.266 · 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

Citations8
Published2004
Admission routes1
Has abstractyes

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