Cascadia Revisited from European Case Studies: the Socio-Political Space of Cross-Border Networks
Bibliographic record
Abstract
In this paper, I seek to analyse how cross-border spaces are constructed through the activities and strategies of established and emerging cross-border networks. In order to observe cross-border actors and public policies, I use three case studies, two in the European Union, i.e. the Rhineland Valley, also known as Upper Rhine (France-Germany-Switzerland) and the Mediterranean Euroregion (France-Spain), and one in North America, i.e. Cascadia (Canada-United States). I propose to draw our theoretical approach from a model suggested by P. Bourdieu, so that it is possible to compare a series of factors that structure these borderlands. The ultimate goal of this paper is to sketch the socio-political space of these networks in each cross-border region and eventually to suggest new research lenses for Cascadia.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".