Bibliographic record
Abstract
This paper provides a novel exploration of extreme global poverty using an original framework that combines critiques of cruelty with the concept of necro-economics. Departing from conventional perspectives on inequality and poverty that accept both as necessary, I contend that global inequality is inherently cruel in outcome and in the becoming cruel of necro-economic subjects. Necro-economics, conceptualized by Warren Montag, is an underutilized concept within the broader biopolitical discourse. Like necro-politics, necro-economics emphasizes the centrality of death within modern economic apparatuses. I show how the concept can be usefully applied to understanding the global economy. The study re-evaluates two perspectives on global poverty (human rights and structural violence) and suggests that both are inadequate for explaining the role of cruelty within the system. To address this gap, I turn to the work of Zygmunt Bauman to show how the global economy, as a necro-economic system engenders a process of cruel subjectivation. The necro-economic framework developed within this paper reveals the global economic system to be one that requires suffering from those at the margins while simultaneously cultivating a callous indifference amongst those at the center. This occurs through self-reinforcing processes of distancing and substituting technical for moral responsibility. In conclusion, the paper establishes that the global economic structure perpetuates suffering through the becoming cruel of necro-economic subjects, shedding new light on the intricate relationship between capitalism, inequality, and human suffering.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".