The Political Economy of Cross-Scale Networks in Resource Co-Management
Bibliographic record
Abstract
We investigate linkages between stakeholders in resource management that occur at different spatial and institutional levels and identify the winners and losers in such interactions. So-called crossscale interactions emerge because of the benefits to individual stakeholder groups in undertaking them or the high costs of not undertaking them. Hence there are uneven gains from cross-scale interactions that are themselves an integral part of social-ecological system governance. The political economy framework outlined here suggests that the determinants of the emergence of cross-scale interactions are the exercise of relative power between stakeholders and their costs of accessing and creating linkages. Cross-scale interactions by powerful stakeholders have the potential to undermine trust in resource management arrangements. If government regulators, for example, mobilize information and resources from cross-level interactions to reinforce their authority, this often disempowers other stakeholders such as resource users. Offsetting such impacts, some cross-scale interactions can be empowering for local level user groups in creating social and political capital. These issues are illustrated with observations on resource management in a marine protected area in Tobago in the Caribbean. The case study demonstrates that the structure of the cross-scale interplay, in terms of relative winners and losers, determines its contribution to the resilience of social-ecological systems.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".