Thinking Small: Stewarding the Artic Commons through Interlocal Institutions
Bibliographic record
Abstract
"This paper aims to broadly address issues of a Northern Commons by more narrowly contributing to the understanding of the institutional performance of natural resource management mechanisms in interlocal commons. How do governments and their constituents design effective environmental institutions to protect natural resources or to clean up existing degraded sites when two countries contiguously share the resource? How successful can these institutions be in the long run at achieving their goals? Can interlocal institutional arrangements produce changes in broader social practices? How do current theories measure the successes and failures of such efforts and what policy reforms might be offered? The intent of my study is theoretical, to create a model for interlocal environmental stewardship by examining the issues related to thinking of the North as a commons. I come at this topic from earlier research based on empirical studies of three transboundary natural resource institutions for water quality between Ontario, Canada and Michigan, United States in the Great Lakes Basin. My study of the fifteen year effort of the Binational Remedial Action Plans in the Detroit, St. Marys, and St. Clair Rivers to protect and remediate critical pollution sites with significant stakeholder involvement led me to theorize about interlocality and the artificial nature of boundaries in relationship to ecology. In this paper I hope to address some key theoretical issues of the Northern Commons and introduce four interlocal arrangements between the United States and Canada that shed light on the possibilities of 'commons' style regional institutions in the North."
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".