Beyond criminocentric dogmatism: Mapping institutional forms of punishment in contemporary societies
Bibliographic record
Abstract
This article aims to question the role criminal justice and criminal law play in structuring the set of dominant conditioning questions in criminology. Based on socio-legal approaches and past fieldwork in immigration control, I argue that the punitive use of non-criminal-based normative systems (such as immigration law) is not a new trend and that, therefore, we are not assisting a ‘criminal contamination’ of other justice systems, but the re-emergence and consolidation of different punitive logics. In that sense, I suggest that criminal justice acts as an epistemological obstacle, being a major barrier to perceive such nuances. Instead, I propose a wider conception of the penal field which operates as a mobile (kinetic sculpture) and includes the criminal law realm, but also other institutional normative systems that configure ‘less’ prominent locations of punishment, such as: regulatory criminal law, civil courts, immigration law, military law, parole boards and other administrative legal systems that play an increasing role in social reaction. I ultimately argue that criminologists should also focus on such administrative-based justice systems in order to better address and resist punitiveness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".