Bibliographic record
Abstract
Abstract Over two centuries of shared border history, the Canada-United States border crossing experience has constantly evolved and been transformed through domestic political practices and international events. The border, as it has in the post-9/11 era, will (and is) necessarily be redefined in a post-COVID-19 world. Indeed, in 2020, the combination of a pandemic and the lack of health coordination at the continental level placed the border back at the heart of the debate, becoming the national health bulwark at the expense of states’ international obligations towards asylum seekers and refugees. The rapid closure of borders, has trapped many people on the move (tourists, seasonal migrants, snowbirds, refugees, displaced persons) outside their national territory or area of residence, thus underlining the fragility of all mobility. This evolution of global borders is decisive for the Québec-American relationship, articulated in recent times around a certain idea of a fluid border demarcation line. In recent times the Québec-U.S. border has changed substantially: This chapter will show that not only did the pandemic episode confirm an evolution that was initiated on September 11, but it also paved the way for an inevitable hardening of this part of the border.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".