COVID-19 restrictions and recreational fisheries in Ontario, Canada: Preliminary insights from an online angler survey
Bibliographic record
Abstract
The COVID-19 pandemic and corresponding public health mitigation strategies have altered many facets of human life. And yet, little is known about how public health measures have impacted complex socio-ecological systems such as recreational fisheries. Using an online snowball survey, we targeted resident anglers in Ontario, Canada, to obtain preliminary insights on how the pandemic has impacted recreational fishing and related activity. We also explored angler perspectives on pandemic-related restrictions and other aspects of fisheries management. Our results point to the value of recreational fisheries for the mental and physical well-being of participants, as well as the value and popularity of outdoor recreation during a pandemic. Although angling effort and fish consumption appeared to decline during the early phases of the pandemic, approximately 21 % of the anglers who responded to our survey self-identified as new entrants who had begun or resumed fishing in that time. Self-reported motivations to fish during the pandemic suggest that free time, importance to mental and physical health, and desires for self-sufficiency caused some anglers to fish more, whereas a lack of free time, poor or uncertain accessibility, and perceived risks caused some anglers to fish less. Respondents also expressed their desires for more clear and consistent communication about COVID-19 fishing restrictions from governments, and viewed angling as a safe pandemic activity. Information on recreational angler behaviours, motivations, and perspectives during the pandemic may prove valuable to fisheries managers and policy makers looking to optimize their strategies for confronting this and other similar crises.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".