Revealing inadvertent elitism in stakeholder models of environmental governance: assessing procedural justice in sustainability organizations
Bibliographic record
Abstract
Consensus-based multi-stakeholder forms of environmental governance involving government, private and civil society actors, have become popular for advancing sustainability, but have been criticized for failing to achieve procedural justice objectives including recognition, participation and strengthening capabilities. Yet, how such models have functioned within non-governmental organizations dedicated to advancing sustainability has been underexplored. This paper assesses the procedural elements of consensus-based multi-stakeholder models used within Canadian biosphere reserves and model forests, two organizations working to address environment and sustainability issues. We draw on strategic documents and semi-structured interviews from five organizations in Canada to analyze their governance structures and processes against a framework for procedural justice. We find the organizational structure reproduces elitism and professionalism associated with stakeholder models more generally and reproduces challenges associated with recognition, participation and building capabilities found in other stakeholder approaches. Meeting broader sustainability challenges requires organizations to address procedural justice issues in addition to their traditional environmental concerns.
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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 | 0.092 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".