Trade Rout: As Trade Tensions Built Between U.S. And Canada, This American Border Town Felt The Freeze
Bibliographic record
Abstract
A close business relationship with Canada has allowed Plattsburgh to thrive as a manufacturing town. Over 100 Quebec-based companies operate there, and 15 percent of the workforce in the county gets its paycheck from a Canadian company. Even more impressive, this comes at a time when manufacturing jobs are declining nationwide. While factories are shutting down across the rust belt, in Plattsburgh they are opening up. There is just one problem. All of this was made possible by the North American Free Trade Agreement, or NAFTA. And just this April, the Trump administration abruptly threatened to pull out of the agreement, leveraging tariffs on Canadian goods and making business between the two countries much more costly. So far, companies like Novabus have absorbed whatever price increases have come their way, and the USMCA has provided some relief, but the situation is far from over. The protracted and ongoing trade tensions, not to mention the bitter rhetoric between the two countries, has caused significant harm to the cross-border relationship, and the future of Plattsburgh’s economy is still uncertain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.010 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".