Emergence of the North American center of excellence for transportation equipment
Bibliographic record
Abstract
Following the closure of Plattsburgh Air Force Base in 1995, the northeastern region of New York State faced a unique economic development challenge. In addition to the loss of the air base, the rural area suffers from urbanization and automation in manufacturing trends. While the quality of life is highly rated, population and job growth stagnate. Isolated geographically by Lake Champlain to the East, the Adirondack Mountains to the South and West, and long distances to southern economic centers in the state, the region has looked north of the Canadian border and positioned itself as "Montreal's US suburb". Economic developers have crafted bi-national agreements between regional organizations, improved cross-border infrastructure, and enhanced educational institutions for the purpose of attracting Canadian and international manufacturers to the region. In 2015, the North American Center of Excellence in Transportation Equipment was launched and six new companies joined the cluster, doubling its size and perhaps providing a base for further growth. Manufacturing jobs are likely to grow for the first time in more than 20-years. We use cluster theory to argue that this formation of companies may still be insufficient to catalyze cluster emergence and the desired goal of regional competitiveness. Moving forward in the crafting of regional economic development policy, we emphasize the importance of viewing the North American Center of Excellence for Transportation Equipment as a pre-emergent cluster in need of further support to reach its potential.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".