The New Reality in Canada/U.S. Relations: Reconciling Security and Economic Interests and the Smart Border Declaration - U.S. Speaker
Bibliographic record
Abstract
Good morning everyone.I am honored to be here today talking about this very vital issue.I am sorry I am not Tom Ridge, but I will try to give you my tour de force on these issues.They certainly have raised to the forefront the critical topic of this conference: how do we sustain this relationship in the context of a new threat environment?I want to make three points today.There is still a security imperative.That is why advancing security has been a part of our matrix.Secondly, this is not a trade-off issue.It is not a balancing act.Advancing security and the economic integration must be done concurrently or it is self-defeating for both missions.I would also like to throw out at you some next steps; how we advance this actual agenda. SECURITY IMPERATIVE
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".