Exploring Environmental Stewardship in the Niagara Region of Canada: How Do Elements of Environmental Stewardship Relate to Success?
Bibliographic record
Abstract
Environmental stewardship is imperative as it provides a means for individuals and society to reconnect with the biosphere as well as work to protect and conserve the environment for future generations. While the concept of stewardship is not new, the scholarship addressing it is still developing. In particular, there is limited research that addresses what makes stewardship successful. This thesis addresses calls in the literature for empirical investigations into local-scale environmental stewardship. Specifically, it contributes to a better understanding of elements of stewardship and what makes stewardship initiatives successful. Two studies were conducted in the Niagara Region of Canada. The first study investigated the social-ecological context of the area and examined the elements of environmental stewardship initiatives by empirically testing a framework for environmental stewardship. The second study examined factors allowing for stewardship success, from the perspective of the organizations conducting the work. In concert, the findings reveal: a nuanced relationship between context and stewardship elements; factors making for stewardship success; and an expanded conceptual framework which more fulsomely describes local environmental stewardship. Finally, recommendations for future work in this realm of empirical environmental stewardship investigations are put forth.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".