Ecological Restoration on the Oak Ridges Moraine: in the context of provincial policies in the GTA
Bibliographic record
Abstract
Conducted an qualitative analysis of ecological restoration (ER) on the Oak Ridges Moraine (ORM). Evaluated the evolution of ER practices from the inception of the Oak Ridges Moraine Act (ORMA) in 2001 to the recent (2015-2017) co-ordinated review of the Oak Ridges Moraine Conservation Plan (ORMCP). Developed a conceptual framework based on ER in the academic literature and compared it to themes uncovered from document analyses, along with three interviews to validate and refine my findings. Main findings of this research were that: \n•\tEnvironmental values and balancing the protection versus development dichotomy are key parts of planning for ER and making it possible to achieve ER projects on the ground. \n•\tDeveloping networks of communication and education, over the course of multiple years, is required to keep local landowners informed and aware of the importance of ER on the ORM. \n•\tProvincial policy and ER projects implemented by the Oak Ridges Moraine Foundation (ORMF) and its partners both seem to highly emphasize the natural components of the ORM environment. \n•\tBoth provincial policy and ER private-public partnerships have complementary and necessary roles in stewardship on the ORM. \n•\tThe ORMF has proven, through multiple ER projects, to be effective in advocating for the ORM environment and local residents’ best interests. \n•\tEvidence and themes extracted makes the claim that the ORMF, although a top-down government and industry funded foundation, can only work successfully if driven by local citizens from the ground up. \n•\tER implementation requires funds and expertise from a wide range of partners. \nThis research concludes with some recommendations for both ER practitioners and stakeholders involved in the development of environmental protection policies in southern Ontario to consider.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".