Narratives of Punishment: Neoliberalism, Class Interests and the Politics of Social Exclusion
Bibliographic record
Abstract
This is a response piece to Eoin O'Sullivan's article Varieties of Punitiveness in Europe: Homeless and Urban Marginality in which O'Sullivan challenges the grand narrative according to which the punitive turn in Europe can be explained by reference to neoliberal policies originating from the United States or to socio-economic and cultural changes associated with late modernity. Instead, O'Sullivan suggests that we should rather speak of varieties of punitiveness based on “distinctive cultural, historical, constitutional and political conditions” in Europe (p. 75) and that the adoption of punitive measures developed alongside more inclusionary measures adopted by a majority of EU member States relying on relatively generous social democratic welfare regimes. While I agree with most of O'Sullivan's analysis, I make two arguments in response. First, although neoliberalism and theories such as broken windows cannot directly explain the creation and enforcement of punitive measures, they certainly have been used as legitimating discourses to justify existing repressive practices worldwide. Moreover, structural constructivits explanations to the management of homelessness and urban marginality remain useful to build local relationships and see how they interact with global narratives. Second, we should acknowledge the existence of relief programmes and public welfare policies historically and in the modern era, but we should also remember that they are often neutralized by counterproductive punitive strategies.The tensions and ambivalence between inclusiveness and exclusiveness may be explained by reference to the distinction between the deserving and underserving poor.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.090 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".