Jude McCullough and Sharon Pickering, eds., Borders and Crime: Pre-Crime, Mobility and Serious Harm in an Age of Globalization.
Bibliographic record
Abstract
This edited volume contributes to critical scholarship on transnational crime control, mobility, citizenship, and border governance.The chapters marshal primary research to reveal the dynamics that operate to foster or disallow transnational human mobility.Each chapter provides an account of how the international border is configured as a zone of negotiation and contact where issues of citizenship and identity, race, gender, and mobility are at stake.Borders and Crime will be of interest to sociologists, critical criminologists, and critical security scholars who study migration and its control, flows of capital and commodities, the globalization of criminal justice initiatives, biosecurity, the geographies of the global war on terror, and international crime and its construction.Borders and Crime begins with an introduction by the volume editors, Jude McCulloch and Sharon Pickering, who describe the contradiction at the heart of the border: it is at once the foremost site where forces of criminalization and crime control operate on a transnational scale as well as a threshold crisscrossed by state actors, entrepreneurs, corporations and other powerful stakeholders that cause widespread harm.This contradiction is positioned as an incongruity between what the authors describe as hyperactivity and hypoactivity at the border.McCulloch and Pickering situate the border as a site of contact and negotiation where coercive power is concentrated and where the demands of effective control and regulation inevitably exceed the capacity of the border to deal with them.The volume is organized in two parts that in turn address the production and construction of crime and organized responses to it, as well as notable failures in addressing transnational criminal activity.The bipartite organization of the volume mirrors another contradiction identified by the border studies literature: namely, that borders are significant for the forms of policing undertaken at them as well the precrime measures that operate across them.The first part of the volume addresses hyperactivity at the border, and offers a series of detailed accounts of the role of the border in defining and addressing crime, threat, and unease.This focus implicitly
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".