After the crisis: new directions in theorising corporate and white-collar crime
Bibliographic record
Abstract
About the book: \n \nThis edited collection brings together established global scholars and new thinkers to outline fresh concepts and theoretical perspectives for criminological research and analysis in the 21st century. Criminologists from the UK, USA, Canada and Australia evaluate the current condition of criminological theory and present students and researchers with new and revised ideas from the realms of politics, culture and subjectivity to unpack crime and violence in the precarious age of global neoliberalism. \n \nThese ideas range from the micro-realm of the ‘personality disorder’ to the macro-realm of global ‘power-crime’. Rejecting or modifying the orthodox notion that crime and harm are largely the products of criminalisation and control systems, these scholars bring causes and conditions back into play in an eclectic yet thematic way that should inspire students and researchers to once again investigate the reasons why some individuals and groups elect to harm others rather than seek sociability. This collection will inspire new criminologists to both look outside their discipline for new ideas to import, and to create new ideas within their discipline to reinvigorate it and further strengthen its ability to explain the crimes and harms that we see around us today. \n \nThis book will be of particular interest to academics and both undergraduate and postgraduate students in the field of criminology, especially to those looking for theoretical concepts and frameworks for dissertations, theses and research reports.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".