Stakeholder engagement: A foundation for natural heritage systems identification and conservation in southern Ontario
Bibliographic record
Abstract
In southern Ontario, multiple organizations apply various approaches to identifying natural heritage systems (NHS). Natural heritage systems comprise a network of natural features and areas, such as protected areas, forests, wetlands, river corridors, lakes, and meadows, as well as the associated natural processes to be conserved and/or managed for various environmental and public services. The application of a variety of approaches can lead to a lack of connections between natural heritage features across political jurisdictions. To further complicate the situation, not all municipalities have the necessary tools and information available to identify and protect NHS nor do they have the capacity to coordinate designing NHS with neighbouring jurisdictions. To address these challenges, a new approach was developed and tested that engages many stakeholders in the collaborative design of a NHS for an ecologically based landscape that crosses several political boundaries. Engagement is an opportunity to work together on common goals with stakeholders, communities, and citizens to find solutions to complex problems and move beyond the traditional consultation that government has used extensively in the past. We engaged a representative group of stakeholders to design and map a scientifically based, quantitatively derived NHS. The engagement process alternated data preparation and analysis activities with target-setting and decision-making by a diverse group of stakeholders, including municipalities, government agencies, non-governmental organizations, stewardship groups, landowners, and other interests. Throughout the target-setting process, observations and feedback from the stakeholders were collected. This paper both documents a number of lessons learned through the engagement process, and demonstrates that stakeholder engagement in NHS design has great potential to coordinate conservation efforts across political jurisdictions and the varied mandates of several organizations.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".