From pity to fear: security as a mechanism for (re)production of vulnerability
Bibliographic record
Abstract
Vulnerability is not only a shared basic condition, but also a condition of potential. In the context of disasters and crises, the concept of vulnerability is frequently used to portray individuals and groups as 'weak', 'threatened', and 'in need of help'. Occasionally, though, a shift occurs and the 'threatened'-and therefore usually the pitied-become those who are feared and hated, that is, they become a 'threat'. This paper explores how apparently incompatible discursive regimes of 'threatened' and 'threat' intertwine, merge, and feed upon each other, and how vulnerability can be and is consequently securitised. It demonstrates that too often the freedoms and opportunities prescribed by the neoliberal state are impossible to actualise when 'normality' and hence 'otherness' are also defined by the state, where people are first and foremost subjects of a global market. These considerations are critical if we are truly to reduce vulnerabilisation by focusing on justice.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.072 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".