Exploring the context and elements of local environmental stewardship: An embedded case study of the Niagara Region, Canada
Bibliographic record
Abstract
Environmental stewardship—a concept that describes the relationships between humans and the environment—is gaining increased attention as an approach that can address planetary sustainability issues. In‐depth empirical investigations of local environmental stewardship are needed to understand how social‐ecological context influences stewardship, as well as the arrangement of conceptual elements in applied settings. This study addresses these needs by conducting an in‐depth exploration of environmental stewardship in the Niagara Region of Canada. A single embedded case study design is employed, with environmental stewardship initiatives constituting the individual units of analysis with the case. Analysis of the spatial arrangement of a total of 89 initiatives indicated that initiatives tended to cluster closer to the Niagara River and in more populous municipalities. The significance of collaboration, tensions between the environment and economic development, and concerns about political impacts emerged as themes across contextual factors. The configuration of stewardship elements reveals interesting discrepancies between initiatives and previous stewardship research focused on larger scales, individuals, and organizations. Further analysis is encouraged to illuminate environmental stewardship in other settings as well as advance relational understanding of conceptual elements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".