Cross Border Innovation Economies: The Cascadia Innovation Corridor case
Bibliographic record
Abstract
In the recent literature on economic geography, cross-border regions have been highly heralded as potential sources for reaping the benefits of innovation (OECD, 2013). In fact, those regions have gained a reputation as being endowed with comparative advantages to compete in global markets (Vance, 2012). However, the types of processes that are occurring in the region, which act as hindrances (or barriers) to cross-border knowledge flows, have remained a significant but understudied topic in the academic literature. The same lack of understanding is widespread among the policy makers engaged in cross-border issues, specifically in terms of improved Cross Border Cooperation (CBC) management. This research project addresses this timely topic by evaluating the effects of the international border between Washington State, U.S. and British Columbia, Canada. This cross-border region, also known as “Cascadia,” possesses a unique combination of assets, including human capital, universities, investments, and financial capital, that enable the cross-border region’s innovation economy to compete globally (Andersen & Wenstrup, 2016). These assets have been supported by local public and private actors (Brunet-Jailly, 2008) and targeted innovation policies aimed at promoting the region as a world-class innovation hub. The object of this study is the Cascadia Innovation Corridor, a current innovation initiative in the region. I adopt a multidisciplinary approach to this case study, combining an economic geography perspective (different forms of proximity have been evaluated in the region), the border policy standpoint (governance implemented in the region) and a regional planning viewpoint (legacy of the Corridor and improvements to the overall strategy to strengthen the collaboration across the border). The research focuses on how tech economies are driving local economic development in Cascadia. This in-depth analysis pursues two goals, both of which are timely contributions to regional efforts: first, identifying the main drivers and hindrances affecting cross-border innovation linkages in the region; and second, developing policy recommendations that will support tighter cross-border economic cooperation. This project is based on primary data collected through a survey and interviews as well as secondary data gathered by official documents (e.g. Memorandum of Understanding further recalled), local newspapers and organizations’ reports. The work empirically gauges the ongoing degree of economic interactions in Cascadia on both sides of the border, examining the networks that exist between organizations and actors involved in the cross-border ecosystem, as well as the missing links that impede stronger collaboration. The final part of the analysis digs into the regional planning practices in the cross-border context and establishes a set of policy recommendations targeted at the cross-border cooperation process in Cascadia. This analysis confirms that the Cascadia innovation ecosystem possesses the key assets needed to ensure long-term growth. Moreover, it sheds light on the role of multinational companies which play a pivotal role in the Cascadia innovation ecosystem, which in turn still appears very ii fragmented. The analysis of the hindrances confirms that transportation infrastructure represents a shortcoming for regional development. From a policy standpoint, the federal-level U.S. political climate does create a burden impacting the economic linkages across the border in Cascadia. Finally, the analysis suggests that the role of local (city) governments is advocated to be more efficient in creating “horizontal” relationships across the border.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".