Obstacles to an integrated, joint forces approach to organized crime enforcement
Bibliographic record
Abstract
Purpose This paper aims to explore some of the problems that arise in the execution of a joint force operation (JFO) involving various law enforcement agencies. Particular emphasis is placed on examining factors that impede inter‐agency cooperation and coordination in the context of a JFO targeting serious and major crimes. Design/methodology/approach This paper is informed by primary research that assessed the level of satisfaction of enforcement agencies involved in a Canadian‐based multi‐agency task force mandated to combat organized crime. Research for this study entailed a questionnaire survey of, and semi‐structured interviews with, operational and supervisory personnel assigned to the JFO, as well senior management within agencies participating in the JFO. Findings The research uncovered significant differences in the level of satisfaction with the execution of the JFO concept between members from the lead (federal) enforcement agency and those of other participating (municipal and provincial) agencies. The majority of survey participants overwhelmingly believe that the integrated, multi‐agency approach is an essential ingredient in the effectiveness of this JFO. However, among respondents from participating agencies there was a high rate of dissatisfaction with intelligence dissemination and sharing by the JFO, communication between the JFO and member agencies, and the contribution the JFO makes to the priorities and outputs of participating agencies in their own jurisdictions. Practical implications These problems strike at the very heart of a multi‐agency approach to major crimes enforcement and can be generalized to other jurisdictions and countries. Indeed, impediments to the timely sharing of criminal intelligence continue to constitute one of the most significant obstacles to inter‐agency cooperation and coordination, and, by extension, the optimal enforcement of organized crime and terrorism. The problems addressed in this study should be of concern to any manager of a multi‐agency task force, and similar research is recommended to unearth problems that may undermine inter‐agency cooperation and plague the effectiveness of a JFO. Originality/value Despite the increased prevalence and importance of multi‐agency operations in combating major and serious crimes, little research has been conducted into the issues and problems that obstruct inter‐agency cooperation within this context. This paper represents one attempt to fill this void.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".