Highway criminal interdiction in Australia: Victoria Police crime and traffic connecting on highways (C.A.T.C.H.)
Bibliographic record
Abstract
Highway criminal interdiction is a law enforcement strategy which enhances police officersr observational, conversational, listening and investigative skills when intercepting and searching vehicles. Highway criminal interdiction has been extremely effective across Canada and North America over the past 20 years as a means of interrupting criminal activity on roads. In Canada alone, the strategy has resulted in contraband and drugs seizures in excess of $4 billion. The strategy involves training law enforcement officers to identify indicators of criminal behaviour and increase their observational, conversational, listening and investigative skills during a vehicle intercept. This strategy was introduced to Australia by Victoria Police in 2010 and titled 'Crime and Traffic Connecting on Highways' (C.A.T.C.H.). This strategy has significantly impacted on road policing and other criminal activity. Since introduction to Victoria Police, C.A.T.C.H. training has been delivered to 3,000 law enforcement officers from Australian Customs and Border Protection Service and the Victoria, New South Wales, and Queensland Police Services. Detections of criminal behaviour on Australian highways as a result of C.A.T.C.H. range from high risk driving behaviours through to large seizures of drugs and other contraband. Total seizures to date amount to A$37 million and climbing. C.A.T.C.H. provides law enforcement agencies with a significant tool in efforts to disrupt criminal activity because at some point criminals and their activities are on our roadways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".