Morality & reputation in the mobilities policing of goods and money
Bibliographic record
Abstract
In the areas of crime control and security, devolution by public institutions to private actors have famously been characterized by David Garland as a responsibilization strategy. Where central governments are “seeking to act upon crime not in a direct fashion through state agencies (police, courts, prisons, social work etc.) [they instead choose to act] indirectly, seeking to activate action on the part of non-state agencies and organizations”. Considering our respective fieldworks in the cross-border logistics and the banking industries, both located in Canada, we propose to revisit this responsibilization thesis to discuss how inquiries into the pluralized production of security need theoretical tools more suited to the kinds of mobility policing these universes are producing.In this communication, we look at how the production of security in these ‘sectors’ is made possible by the meeting of two universes of practice, the first firmly anchored in the economy and the other in policing, both known to respond to supposedly distinct rationalities. Yet since banking and logistics are now conversing daily with security authorities through data, these universes are now connected by what we call a politics of revelation where the professionalization and normalization of self-disclosure, but also its insertion in a moral economy that normatively stresses public duty to anchor those new obligations, generates novel relations of interdependence between economic and security actors. The meeting of these actors has return effects on their respective fields internal orders, producing both new reputational stakes for these industries and economic guardianship duties for security authorities. Ultimately, these interconnections create feedback loops which leave neither field unscathed, restructuring them in ways that remain open to inquiry.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".