Arrest and Detention in International Travelers
Bibliographic record
Abstract
BACKGROUND: Our objective was to examine the characteristics of international travelers from Canada who have been arrested or detained while abroad, and to review the health implications of incarceration. METHOD: An EpiInfo 6 program was created to analyse all of the Consular reports received in 1995 via the Secure Integrated Global Network (SIGNET) which provides communications and computerization services to the Department of Foreign Affairs and International Trade, Canada. The Consular Management and Operations System was designed to support the delivery of consular services by the Department, and to link Headquarters in Ottawa with missions in other countries through case management files, including a "Prisoners" file. Information obtained included personal demographics (age, gender), date, country, and reason for arrest or detention, and outcome of judicial process. RESULTS: There were 1, 086 arrest or detention reports received from Consular services via SIGNET in 1995. Males outnumbered females 5.6:1. Most individuals arrested were young: 57.5% were less than 40 years, and 79% were less than 50 years. Drug related charges were cited in 33.1% of all cases, with 52.8% of arrested females charged with drug related offenses. The documented conviction rate was 96%. The majority of detained Canadian travelers were held in countries within the Americas (791 cases - 69.2%), with 642 (59.1%) being detained in the USA. CONCLUSIONS: Arrest and detention is an unusual occurrence for international travelers but relative youth, male gender, and female drug couriers were identifiable risk characteristics. Public awareness campaigns can be targeted to specific population demographics, but all international travelers need to be counseled on the consequences of transgressing laws in foreign countries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".