A Deleuzian critique of resource‐use management politics in Industria
Bibliographic record
Abstract
In an era of increasingly well‐attended and violent protests around the world against globalisation, this commentary seeks to answer the crucial question: The globalisation of what? The answer proposed is that what is globalising is ‘Industria’, a multiplicitous, global system of power/knowledge, a vast ‘machinic assemblage’ recently accreted from diverse, competing world systems. After describing some of the most serious challenges facing human communities and the rest of the biosphere, the commentary enlists the aid of the philosopher Gilles Deleuze and his colleague Félix Guattari to expose the cognitive errors underlying political and environmental problems. Applying Deleuze's philosophy of difference to geopolitics and resource‐use management, it is shown that representational epistemologies and a negative ontology of identity obscure the myriad interconnections among human and non‐human beings, leading to conflict and ecological degradation. From there, the ‘Industria’ hypothesis is presented as a conceptual response to Deleuze and Guattari's critique of the ‘Urstaat’ and as a framework for scholars grappling with the need to achieve socioeconomic and ecological sustainability. Finally, the commentary briefly explores the potential of a differential, bioregional geopolitics as a civilised alternative to the predations of Industria.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".