A framework for analyzing institutional gaps in natural resource governance
Bibliographic record
Abstract
In this paper we present the Inter-Institutional Gap(IIG) Framework as a novel approach to conceptualizing the often-overlooked interconnectivity of different rule-levels between formal and informal institutions in a resource system. This framework goes beyond the existing concepts of legal pluralism, institutional void, structural hole, and cultural mismatch, each of which offer valuable insights to particular gaps between formal and informal institutions, but do not sufficiently address the interaction at every rule level (i.e. constitutional choice, collective choice and operational choice rules). In order to demonstrate the potential of our framework for better understanding the underlying causes of inter-institutional gaps, we apply it to four case studies that encompass diverse geographical locations, governance scales, and social-ecological systems. Results reveal inter-institutional gaps can be created when there are unintended, unforeseen or hidden gaps between different rule hierarchies in two or more simultaneously operating institutions. More specifically we observe that: i) inter-institutional gaps are co-existing, therefore if a certain gap is identified, other gaps may be expected; ii) certain gaps may reveal latent gaps; and iii) intermediaries may be key to addressing inter-institutional gaps. In many cases, sustainable natural resource management and regulation cannot be achieved without directly addressing the inter-institutional gaps that exist between formal and informal institutions operating in the same resource system. The Framework facilitates analysis and understanding of multi-level governance structures in pursuit of addressing complex natural resource management issues.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".