Cascadia: The (Re)construction of a bi‐national space and its residents
Bibliographic record
Abstract
This paper aims to demonstrate how globalization and discourses on regional/national identities cannot only create cross‐border/regional social spaces but also the criteria to select a transnational elite to occupy the cross‐border space reified by interplay of myths and logic. Using the case of Cascadia, we observe a construction of regional social space, taking place along the Pacific Northwest border of U.S. and Canada, through the process of globalization. In this socially constructed region of Cascadia, two often‐antagonistic groups are mutually benefiting from each other by creating a unique bi‐national space. On one hand, the neo‐liberal business community is redefining borders in terms of free trade while on the other hand the environmentalists are redefining borders in terms of eco‐systems. However, to create and maintain this regional identity and redefinition of transnational space an effective transportation conduit is required. Because transnational travel requires a high level of governmentality to control the flow of goods and people, the construction of Cascadia and its concomitant transportation corridor, the groups involved simultaneously are creating an “othering” process. Hence, this reconstruction of bi‐national space essentially shows how discourses on nationalism and internationalization co‐exist, reinforce each other, and are often sub‐processes of globalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".