Bibliographic record
Abstract
openly.Justin Trudeau, our new prime minister, announced that we're going to eliminate that requirement, and we're working together with the Mexican government on a plan to do just that.lc: And in the future, we'll have something similar to what the Europeans have vis-à-vis the United States, won't we?They call it the esta, don't they?pa: Yes, the Electronic System for Travel Authorization (esta).We're initiating that program March 15, but not with all countries because it's a pilot project, though eventually we will include everyone.We have to be very clear about this: this is not an instrument we're going to impose on the Mexicans, but all countries will have to go through this short two-or three-minute-long procedure on line.lc: In effect, the imposition of the visa was an irritant; we can't get away from that.It also slowed down our stupendous cooperation in the flow of students and tourists.But beyond the visa issue, which we have talked about on several occasions, I have the impression that it affected the trilateral vision we had when we celebrated the sixtieth anniversary of our relations; that is, the project of turning the region of North America into the most competitive in the world, when
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.508 | 0.183 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".