Ten bridges on the road to recovering Canada’s endangered species
Bibliographic record
Abstract
Wildlife is declining around the world. Many developed nations have enacted legislation on endangered species protection and provide funding for wildlife recovery. Protecting endangered species is also supported by the public and judiciary. Yet, despite what appear as enabling conditions, wild species continue to decline. Our paper explores pathways to endangered species recovery by analyzing the barriers that have been identified in Canada, the United States, and Australia. We summarize these findings based on Canada’s Species at Risk Conservation Cycle (assessment, protection, recovery planning, implementation, and monitoring and evaluation) and then identify 10 “bridges” that could help overcome these barriers and bend our current trajectory of wildlife loss to recovery. These bridges include ecosystem approaches to recovery, building capacity for community co-governance, linking wildlife recovery to ecosystem services, and improving our storytelling about the loss and recovery of wildlife. The focus of our conclusions is the Canadian setting, but our findings can be applied in other national and subnational settings to reverse the decline of wildlife and halt extinction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".