The application of ecosystem approaches in the Golden Horseshoe Region, Ontario’
Bibliographic record
Abstract
Environmental crises have prompted a re-evaluation of traditional approaches to environmental management that has often highlighted their inability to deal with the complexity of social-ecological systems. The ecosystem approach (EA) offers an alternative that combines ideas from systems theory, participatory decision-making and adaptive management. However, EAs are still relatively young both as a discipline and as a practice, and as such, their meaning is continuously being redefined. This paper assesses the current state of development and the direction of EAs in the Golden Horseshoe Region (Ontario) by analyzing quantitative and qualitative data from interviews with EA practitioners. Practitioners were involved in projects that applied EA in a variety of fronts from ecological restoration to eco-health to urban development. The survey used helped to identify the theoretical foundations and core themes of EA; the methods, techniques and tools used; and, the factors and barriers to its implementation and potential application to other contexts. Two findings emerged from this research: first, despite the diversity of practitioners and applications, the theoretical understandings of EA are coalescing into a unified view that emphasizes the principles of integration, connectivity and participation. Second, successful implementation of EAs will require a parallel shift in the current institutional setting towards more adaptive forms of governance. Despite this obstacle EA continues to spread to applications in the fields of eco-health and urban development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".