Models of Representation and Participation in Model Forests: Dilemmas and Implications for Networked Forms of Environmental Governance Involving Indigenous People
Bibliographic record
Abstract
ABSTRACT Our study of two Model Forests illustrates the complex nature of representation in governance networks, in which different traditions of governance, persistent political disagreement and historical adversaries work together to achieve some goal. Using Pitkin's (1967) models of representation, we examined the role of social practices in giving meaning to representation in two model forests in Canada and in Sweden. Two normative models of representation (e.g., trustee and delegate) guided our interpretation of the enactment of representation in the two model forests mainly by highlighting how such models may be culturally biased–resulting in dilemmas of governance for actors that do not ascribe such meanings to representation. These insights led to a greater appreciation for the significance of politics in the enactment of representation, especially with regards to the political aspirations that motivate participatory and deliberative environmental governance. Our analysis suggests that the legitimacy and effectiveness of natural resource and environmental governance networks are affected by the rules structuring participation and deliberation, which are substantiated in social practices of representation in these networks. Our work further suggests that the analysis of representation in network forms of governance cannot be separated from an analysis of the politics of competing interests, especially whose interests are advanced and how these interests are given voice in steering environmental governance. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".