Helping land trusts prepare for a new climate: experiences of challenges and facilitators for translating knowledge about climate change adaptation in Ontario, Canada
Bibliographic record
Abstract
The environment is changing under the impact of climate change, but many Ontario land trusts still operate with the goal of maintaining historic patterns of biodiversity. This misalignment of conditions and goals may render the important work of these land trusts less effective. To help improve this situation, we conducted several knowledge translation activities to inform Ontario land trusts about possible climate change adaptation options. Throughout the knowledge translation activities, we collected participants’ comments and examined them with hypothesis and descriptive coding to reveal data about challenges and facilitators, which we grouped into themes with pattern coding. The results helped us identify challenges and facilitators that land trusts experience when participating in climate change knowledge translation and attempting to adapt to climate change. The challenges include a lack of resources, limited technical skills and species knowledge, and competing priorities and perspectives. Facilitators include a general interest in climate change, use of tools for adaptation planning, and resource sharing. To increase climate change adaptability in the Ontario land trust sector, we recommend greater collaboration between land trusts, modest modifications to existing conservation actions, shifting from passive to active conservation, and moving from species-level to community and ecosystem function conservation goals.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".