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Record W2148497692 · doi:10.1017/s0008423907070436

Empire and Imperialism: A Critical Reading of Michael Hardt and Antonio Negri

2007· article· en· W2148497692 on OpenAlexaff
Carol A. L. Prager

Bibliographic record

VenueCanadian Journal of Political Science · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEmpirePoliticsReading (process)GlobalizationLawPolitical scienceSociologyEconomic historyPhilosophyHistory

Abstract

fetched live from OpenAlex

Empire and Imperialism: A Critical Reading of Michael Hardt and Antonio Negri , Atilio A. Boron, London: Zed Books, 2005, pp. 141. Michael Walzer, reflecting in a 2002 Dissent article (vol. 49, Spring) upon the compelling issues in world politics, asked “Can there be a decent Left?” After reading Atilio A. Boron's impassioned and derisive critique of Michael Hardt and Antonio Negri's Empire (Cambridge MA: Harvard University Press, 2000), one wonders whether today there can be an empirically sophisticated, coherent Left. (Negri, by the way, spent seventeen years in Italian prisons for his involvement with the Red Brigade and the murder of Italian politician Aldo Moro.) Boron, a professor of political theory at the University of Buenos Aires claims, no doubt rightly, that the last three decades, embracing the end of the Cold War, the impact of neo-liberal policies on the “periphery” and sweeping technological changes, have necessitated a reformulation of leftist thinking. The influential Empire , which advances a root-and-branch restructuring of socialist thought, though hugely popular among anti-globalization groups and already translated into over a dozen languages, is to Boron emphatically not it. While paying obeisance to Hardt and Negri's “noble intentions and intellectual and political honesty” (4–5), the author proceeds to shred virtually all their main contentions.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.007
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.331
Teacher spread0.309 · 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.

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

Citations2
Published2007
Admission routes1
Has abstractyes

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