Bibliographic record
Abstract
9/11 and the COVID-19 pandemic were once-in-a-generation events with profound implications for US–Canada border management. Following September 11, 2001, the two countries collaborated on a border management regime that was highly integrated but also intended as a battle line in the war on terror. At the same time, the need to support growing NAFTA supply chains required creative public policy to keep border efficiency on par with border security. These arrangements set the tone for bilateral border management for nearly 20 years, but by 2020, the two governments were due for a reset. However, instead of planned border modernization, COVID-19 closed the borders to all but essential travel and trade for 18 months, disrupting border communities, casting doubt on the resilience of cross-border supply chains, but also accelerating the use of digital, touchless, border technologies. This chapter examines how border management by two sovereign nations has been driven by crisis, accelerating some actions but also leaving important needs unmet. As the two nations face new challenges of the global migration crisis and the movement of illicit products, this chapter makes the case for mechanisms for joint border collaboration that don’t require a crisis to be effective.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".