Securing cross-border collaboration: transgovernmental enforcement networks, organized crime and illicit international political economy
Bibliographic record
Abstract
ABSTRACT In a globalizing world, cross-border enforcement networks are rapidly emerging as important mechanisms to tackle illicit transnational markets. As a relatively recent mode of cross-border governance, both the IPE and public policy literatures have only just begun to explore the dynamics and implications of cross-border policy networks in general and security networks in particular. Cross-border enforcement networks are similar to current IPE conceptions of transgovernmental networks, yet the comparative analysis of such networks in this article shows that they extend, and differ, from transgovernmental networks. Instead, transgovernmental enforcement networks are emerging as a comparable but distinct transnational model and thus warrant emancipation as an object of study in their own right. By exploring two network cases concerned with US-Canada cross-border tobacco smuggling, the article discerns and describes factors and conditions that account for different outcomes among select U.S-Canada cross-border security networks: IBET/Shiprider and MYGALE. Data was collected by analyzing open primary sources and conducting interviews with subject participants in these policy networks. Based on these observations, the article generates insights that can subsequently be scrutinized using other cross-border policy case studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".