COVID-19 and the US-Canada Border Report 2: Canadians and Taxable Retail Sales within Whatcom County
Bibliographic record
Abstract
On February 29th, 2020, the first death from COVID-19 occurred in Washington State. Over the weeks following, both Washington State and British Columbia implemented various efforts aimed at reducing the spread of the virus. On March 14th, B.C. announced closures of many businesses, made recommendations against non-essential travel, and implemented a voluntary two week self- quarantine on Canadians returning to Canada. Two weeks later, Washington issued a stay-at- home order which went into effect March 23rd. These state and provincial measures aimed at limiting mobility were soon followed by coordinated decisions by the U.S. and Canada to limit cross-border travel. These restrictions, which went into effect on March 21st, placed limits on all ‘non-essential’ passenger travel between the two countries, while maintaining the flow of commercial cargo. This report is one in a series of briefings aimed at improving our understanding of how the border and cross-border activities affect various aspects of Whatcom County’s economy. This is particularly important when considering economic recovery post-COVID-19, as cross-border volumes may remain relatively low even after border restrictions and stay-at-home orders are lifted. The relatively large number of COVID-19 infections and fatalities in Washington State compared to B.C. is likely to influence Canadians’ decisions to engage in discretionary travel to the U.S.1 Analyses presented here illuminate how the short- to medium-term effects of fewer Canadian visitors will likely impact particular sectors in certain locations in Whatcom County in different ways. These BPRI briefings are an effort to describe and measure those impacts. Additional reports consider shopping destinations and tourism.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".