The Resilience of Governance Networks: Wildlife Health Management in Canada
Bibliographic record
Abstract
Emerging diseases in Canada threaten the wellbeing of humans, domestic and farmed animals, as well as wildlife. Canada, like many nations, struggles to manage diseases that cross boundaries, both geographically and in species. This has led to a heavy reliance on governance networks to coordinate the knowledge and resources needed to develop management approaches. As governance networks often exist in an informal or ad hoc capacity and at the same time attempt to solve complex or expansive policy problems beyond the ability of any one agency, the issue of network resilience is examined to explore how networks and their membership can mitigate network failure. Through two case studies of wildlife disease incidents in Canada (Chronic wasting disease and White nose syndrome), I examine how the wildlife health network in Canada developed its disease management approaches as well as recommendations to provincial and federal governments. Using primary sources, I evaluate the network’s activities, attitudes and behaviours to assess if characteristics associated with resilience (slack in resources, adaptive capacity and situation awareness) are present and if they contribute to positive outcomes. Greater presence of resilient characteristics- slack in resources, adaptive capacity and situation awareness-were present in the case with better policy outcomes, however, the analysis reveals that the concept of resilience is limited as a useful tool when examined in the broader context. Governance networks are often limited by the structural constraints of their environment, including scarce resources and a lack of self-determination. In this network, an additional factors exists to complicate analysis: disease type and severity. The relative ease with which an emerging disease can be understood and management appears to contribute significantly to the network’s success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".