Embracing conceptual diversity to integrate power and institutional analysis: Introducing a relational typology
Bibliographic record
Abstract
Within environmental governance scholarship, an increasing interest in integrating the study of power with institutional analysis is generating novel theoretical and empirical perspectives for understanding human-environment relationships. The array of different approaches employed to integrate power into institutionalist work promises a range of insights. However, building a cohesive research agenda depends on efforts to grapple with the conceptual and theoretical diversity that characterizes the study of power. To this end, we introduce a typology of relationships between power and institutions. The typology brings together diverse conceptualizations of power and institutions within a common analytical space and situates them around two overarching research questions: How does power shape institutions? And how do institutions shape power? The structure of the typology aids researchers in generating specific, operationalizable research questions within the broader research agenda on power and institutions. In the paper, we describe the theoretical basis for the development of the typology, which draws on political ecology and Bloomington school institutionalism. Then, we employ the typology to organize a review of environmental governance literature on power and institutions. This exercise demonstrates the utility of the typology not only for organizing the currently disjointed body of work on power and institutions but also for identifying new research questions. Furthermore, it facilitates discussions about deeper ontological, epistemological, and methodological challenges associated with bringing together different theoretical approaches. Ultimately, the typology defines pathways for integrating two important disciplines studying environmental governance, political ecology and institutionalism, and facilitates the accumulation of a coherent body of knowledge.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".