Sieve or Shield? High Tech Firms and Entrepreneurs and the Impacts of COVID 19 on North American Border Regions
Bibliographic record
Abstract
This study examines the role of international borders in the era of the Covid-19 pandemic, which led to unprecedented national decisions to close borders in order to contain the domestic contagion. The idea that borders act as shields conflicts with the needs of cross-border regions, as they rely on networks straddling the borders for goods and services’ provisions. This paper explores different approaches at individual, local, and regional policy levels used to counterbalance such impacts. As evidenced by North American border closures to most non-citizens seeking entry (shield effects), it is important to understand how professionals, firms, and their networks exercised various forms of agency (sieve effects) to negotiate the border and its policies during this most unusual time. Drawing from a comparative study between two North American border regions distinguished for their thriving innovative business ecosystems – Cascadia (Seattle-Vancouver) along the Canada-U.S. border and Calibaja (San Diego-Tijuana) along the Mexico-U.S. border – we seek to understand how COVID-19 measures have influenced cross-border economies through unprecedented responses to crisis management.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".