Canada Update - Highlights of Major Legal News and Significant Court Cases from February 2008 to April 2008
Bibliographic record
Abstract
ANADA'S border agency, Canada Border Services Agency (CBSA), wants buses, trains, and cruise ships to provide electronic lists of passengers and their personal details in advance of their arrival into Canada.'Currently, the CBSA collects information on airline passengers, but companies that operate buses, trains, and cruise ships are only required to provide the information on request and not with the same mandatory electronic transfer.2 This additional collection of passenger details would mean that the CBSA would be able to obtain electronic data on 100 percent of passengers, coming from all modes of travel.3 The personal information that airlines currently provide the CBSA "includes full name, birth date, gender, citizenship, visa and passport numbers, baggage information, and seat number."' 4 A report from the agency stated that they plan to create a single, central authority that would be used to collect, monitor, and analyze this passenger information to spot potential terrorists and criminals.5 Canada currently shares some of this information with U.S. agencies under agreement.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".