Bibliographic record
Abstract
This review essay critically engages three socio-legal books directed to the changing bases of criminalization; namely, Lacey ( In search of criminal responsibility: ideas, interests, and institutions , Oxford University Press, Oxford, 2017 ); Farmer ( Making the modern criminal law: criminalization and civil order , Oxford University Press, Oxford, 2016 ); and Norrie, Justice and the slaughter bench: essays on law’s broken dialectic , Routledge, New York, 2016 ). The texts explore how modern (largely English) institutions of criminal law proscribe, assign responsibility and appear through contradictory socio-political ‘constellations’. They variously reference criminal law’s expanding punitiveness as it: embraces revived character-based ways of attributing responsibility via ideas of risk; drifts away from a social function of creating civil order; and, works through a ‘broken dialectic’ that fails to recognize its ethico-political auspices. The ensuing ‘overcriminalization’ is referenced variously, but this review questions a tendency to work off legal lexicons, with consequent limitations placed on the scope of social analysis. Referring to Roman and Cape colonial forms of criminalization, this review highlights processes of accusation that call subjects to account as criminals, thereby signalling an initiating socio-political layer upon which unequal forms of overcriminalization rest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".