Breaking the Wall of Monocentric Governance: Polycentricity in the Governance of Persistent Organic Pollutants in the Arctic
Bibliographic record
Abstract
Abstract We often mistakenly assume that institutional design will remain effective indefinitely. Complex long-term environmental challenges illuminate the disparity between institutions and state boundaries. While globalization has challenged monocentrism, we must look beyond traditional measures and design resilient governance systems, such as polycentric governance, that combine trust and local expertise in small-scale governance with the governance capacity of large-scale systems. These harness globalization’s benefits and provide solutions for the effects of ecosystem changes. This work examines the lessons – benefits, challenges, limitations, and unanswered questions – that may be learned from polycentric governance in the case of Persistent Organic Pollutants (POPs) in the Arctic, where a polycentric political system has developed as a result of a mismatch in environmental, jurisdictional, and temporal scales. Section One examines characteristics of polycentricity, focusing on actors, multilevel governance, degree of formality, and the nature of interactions. Section Two concentrates on the tools utilized. Section Three applies the outlined framework. Finally, Section Four examines three lessons that global environmental governance may learn from the case study: (1) Peak organizations are effective tools for managing polycentricity, allowing for the inclusion of non-state actors, such as indigenous peoples organizations (2) and epistemic communities (3), in bridging the human-environment nexus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".