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Record W4398186542 · doi:10.1093/isagsq/ksae036

A Realist Ethos of Resistance in Global Politics

2024· article· en· W4398186542 on OpenAlexafffund
Antonio Franceschet

Bibliographic record

VenueGlobal Studies Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council
KeywordsEthosPoliticsResistance (ecology)Political scienceEnvironmental ethicsPolitical economySociologyLawPhilosophyBiology

Abstract

fetched live from OpenAlex

Abstract Realism is conventionally understood as coldly accepting the powerful dominating the weak. Reversing this image, I argue that Realism contains an implicit ethos of resistance. Drawing on a recent scholarship on the historical complexity and diversity of classical Realism in international relations (IR), this article uncovers this ethos by focusing on three shifts of perspective: (1) from an extreme to moderate view of power politics; (2) from naturalizing the status quo to envisaging progressive change; and (3) from a horizontal view of politics among nations (or other horizontally situated entities) to a global image of power politics. I then explore how these shifts exist in a different scholarship, the emergence of a so-called new Realism in political theory. The article builds a conversation between classical Realism in IR and the new Realist philosopher Bernard Williams’ work, finding that both articulate an ethos of legitimate resistance to domination. The significance of the Realist ethos is that it challenges stereotyped images of the tradition justifying domination and injustices, and it inserts and positions a Realist voice in recent debates over human rights to resist in global politics.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.077
Scholarly communication0.0100.009
Open science0.0010.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.371
Teacher spread0.345 · 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 designTheoretical or conceptual
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

Citations3
Published2024
Admission routes2
Has abstractyes

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