Cross border regional planning: insights from Cascadia
Bibliographic record
Abstract
This analysis focuses on different levels of Cross-Border Regional Planning (CBRP) processes in the Cascadia borderland. The region is home to the business-led initiative ‘Cascadia Innovation Corridor’ (CIC), designed to foster cross-border economic integration. The CIC strives to build a global innovation ecosystem in Cascadia, including a new high-speed train to connect Seattle and Vancouver. This paper focuses on the scope of the CIC as a CBRP case. The authors evaluate engagement of city governments and coherency between different planning scales to determine whether the CIC has been addressing the major challenges that may prevent tighter economicintegration in Cascadia. The analysis deploys secondary data as well as primary data collected through surveys and interviews. The results shed light on a discrepancy between supra-regional ‘soft planning’ and the urban planning level. The authors offer an evidence-based proposal to broaden the scope of the CIC from a CBRP standpoint.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".