Confronting a post-pandemic new-normal—threats and opportunities to trust-based relationships in natural resource science and management
Bibliographic record
Abstract
Natural resource governance is inherently complex owing to the socio-ecological systems in which it is embedded. Working arrangements have been fundamentally transformed throughout the COVID-19 pandemic with potential negative impacts on trust-based social networks foundational to resource management and transboundary governance. To inform development of a post-pandemic new-normal in resource management, we examined trust relationships using the Laurentian Great Lakes of North America as a case study. 82.9% (n = 97/117) of Great Lakes fishery managers and scientists surveyed indicated that virtual engagement was effective for maintaining well-established relationships during the pandemic; however, 76.7% (n = 89/116) of respondents indicated in-person engagement to be more effective than virtual engagement for building and maintaining trust. Despite some shortcomings, virtual or remote engagement presents opportunities, such as: (1) care and nurturing of well-established long-term relationships; (2) short-term (1-3 years) trust maintenance; (3) peer-peer or mentor-mentee coordination; (4) supplemental communications; (5) producer-push knowledge dissemination; and, if done thoughtfully, (6) enhancing diversity, equity, and inclusion. Without change, pre-pandemic trust-based relationships foundational to cooperative, multinational, resource management are under threat.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".