Organizing Across the Canada–US Border: Binational Institutions in the Niagara Region
Bibliographic record
Abstract
This article explores the experience of seven grassroots cross-border organizations that have formed in the binational Niagara region. It begins by introducing the binational Niagara region and noting the comparative paucity and weakness of cross-border institutions in the area. It then identifies a number of grass-roots organizational initiatives that in some formal way straddle the Niagara River – initiatives that scholars have typically neglected. Based on interviews with leaders in these organizations, I describe the various governance structures and processes that these organizations have adopted to accommodate Canadian and American interests. I then turn to a discussion of some of the challenges facing these organizations that stem from their binational nature. Two in particular – resulting (1) from the securitization of the border since 2001 and the recent implementation of the WHTI secure document requirements, and (2) from the problems associated with fragmented political environments on both sides of the border – are discussed, and a rough assessment of their relevance to the seven organizations is given. To varying degrees, these factors constrain the opportunities for existing cross-border institutions, and they serve as deterrents for the formation of more such entities. The information uncovered in these inquiries provides a snapshot of the uncertain progress toward the formation of a borderland region centered around the Niagara River in southern Ontario and western New York.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".