Bibliographic record
Abstract
On March 18, the US announced that it would close its northern border with Canada to non-essential traffic. On March 20, the US and Mexico agreed to restrict non-essential travel over their shared border for 30 days. On April 20, the United States, Mexico, and Canada announced they are extending restrictions on non-essential travel across the border. On August 9, 2021, Canada reopened the border, and on November 8, 2021, the US reopened the border, each with different restrictions. In the United States, travel restrictions varied by state. 27 states issued executive orders that placed restrictions on out-of-state visitors. Those restrictions included filling out travel declarations, completing a two-week quarantine, and/or proof of a negative test upon arrival. Further restrictions happened within states with municipality governments and tribal governments taking action that was counter to the state and federal orders. This article will analyze the tension that arose in the United States when conflicting orders from different levels of governments come out. How these different restrictions were in tension between protecting the public health and the ill effects that can come with restricting mobility and lessons we can learn to better prepare to respond in the future.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".