Of Bridges and Borders: An APSA Congressional Fellow's North American Tale
Bibliographic record
Abstract
I had seen once before, halfway around the world, what a new bridge could do to a border region. However, despite having worked in border towns and on border issues for more than a decade, I had never given much thought to the border in my own backyard. I remember crossing the Detroit River to visit the casinos in Windsor, Ontario, before Detroit had her own and seeing the lines of trucks waiting to cross the bridge and being unaware of the border's economic importance to the region and the country. I had studied the economic benefits of European integration, but never thought about how those lessons could be applied to the North American continent. However, last year as a 2011–2012 APSA congressional fellow working with my hometown congressman US Representative Gary Peters I had the opportunity to apply these overseas experiences to a border that mattered to my town, state, and country. The US-Canadian border is not only the world's longest nonmilitarized border, but is also the line separating my hometown Detroit, Michigan, from Windsor, Ontario; and it is the line preventing the region and the two countries from fully realizing their economic 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".