Environmental Resource Management in Borderlands: Evolution from Competing Interests to Common Aversions
Bibliographic record
Abstract
Great enthusiasm is attached to the emergence of cross-border regions (CBRs) as a new institutional arrangement for dealing with local cross-border environmental resource management and other issues that remain too distant from national capitals and/or too expensive to be addressed in the traditional topocratic manner requiring instead local adhocratic methods. This study briefly discusses the perceived value of CBRs and necessary and sufficient conditions for the successful and sustainable development of such places. Then, assuming that necessary conditions can be met, the study investigates an intriguing hypothesis concerning the catalyzing of sustainable consensus for cross-border resource management based on a game theoretical approach that employs the use of dilemma of common aversion rather than the more traditional dilemma of competing common interests. Using this lens to investigate a series of events on the Pacific northwestern Canadian-American border in a part of the Fraser Lowland, we look for evidence of the emergence of an active and sustainable CBR to address local trans-border resource management issues. Although our micro-level scale fails to conclusively demonstrate such evidence, it does demonstrate the value of using this approach and suggests a number of avenues for further research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".