Coupling ecosystem-centered governance modes with environmental justice
Bibliographic record
Abstract
In North America, Great Lakes Areas of Concern (AOCs) were established to remediate aquatic pollution in 1987 as part of a binational agreement between the United State of America and Canada. Although the action preceded formal environmental injustice acknowledgment, the AOC program's effort to remediate legacy pollutants includes language with the potential to accomplish core goals of EJ: democratizing decision-making and reducing disproportionate environmental burden. Yet, in AOCs, discussions of public engagement regarding AOC work tend to define participation institutionally (i.e., the state, market, and civil society) rather than by racial or socioeconomic inclusivity. Understanding how AOC governance processes consider representation of, and benefit to communities negotiating remediation decisions from positions of systemic disadvantage requires addressing the relationship between ecosystem-centered governance modes and environmental justice. In this study, interviews with governance actors reveal that concern for EJ issues wield different forms of authority as ecosystem-centered governance and environmental justice couple, decouple, and uncouple. Changes in coupling correspond with shifts in ecosystem-centric governance mode, but coupling does not rely on any one particular governance arrangement. Instead, coupling relies on leadership practices and conceptions of fairness that are EJ-responsive and present EJ as indistinct from ecosystem goals and targets. Our findings reinforce the assertion that ecosystem-centered governance can be reimagined to better facilitate EJ even without changes in financial and regulatory constraints. We conclude by proposing empirical measures that advance EGM-EJ qualitative scholarship and practical advice about how to cultivate EJ-responsive leadership in ecosystem-centered governance arrangements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".