Empire and Imperialism: A Critical Reading of Michael Hardt and Antonio Negri
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".