Public policy in a cross‐border economic region
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe an economic region straddling the Canada‐USA border between Ontario and Michigan from historical and contemporary perspectives. It aims to highlight policy challenges for federal, state, provincial and municipal governments. Design/methodology/approach This paper provides a general review based on academic literature, government and consultant reports and data from a variety of sources. It begins with a historical review of the study regions. This is followed by a more detailed contemporary review of conditions arising since the attacks of September 11, 2001. A number of possible and ongoing policy options for various orders of government are then described. Findings The paper finds that Ontario and Michigan comprise a highly integrated economic region with a particular focus on automotive production. Within that region the Canada‐USA border is a key transportation bottleneck whose impedance effect has gotten worse in the aftermath of September 11, 2001. A variety of policies have been implemented to try to reduce the cost of the border with mixed success and there is little cross‐border interaction among lower orders of government. Originality/value To the best of the author's knowledge there has been no other paper published in an academic journal that describes the history, current situation and policy issues of the study region. The value of this paper lies in providing a multidisciplinary overview and a starting point for further research on the region.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".