Bibliographic record
Abstract
Abstract There is limited understanding of cruelty in critical security studies. While cruelty tends to be conceptualized within the context of large-scale, violent conflicts and situations, it is helpful to consider cruelty through the lens of everyday forms of violence and subjugation. Understanding cruelty and its complex entanglements with overlapping frameworks of necropolitics, structural violence, and necrogeopolitics, and drawing on research from Nigeria, Jordan, and Myanmar, this article discusses the normalization of cruel, everyday “living death” and violence experienced by many in Global South. Overlapping marginalities of localized conflicts, political repression, gendered violence, marginalized livelihoods and precarity, climate change, and migration illustrate this entangled conceptualization of cruelty. This complex and entangled understanding of cruelty helps to better understand the lived experiences and situations of peoples and communities in the Global South. Further, everyday necropolitical violence and cruelty provide an understanding of the suffering, pain, and state of unease that many experience in the Global South and beyond, and this understanding of shared human vulnerabilities informs our common humanity. The main contribution of this analysis is to provide dialectical insights into the potential of radical empathy and compassion, rooted in decolonial humanism, as a means to ignite political consciousness, dismantle oppressive structures, and support emancipatory agency of peoples and communities globally.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".