Revisiting “Invisible Collapse?”: Perspectives on progress and challenges for the future of recreational fisheries science
Bibliographic record
Abstract
More than 20 years ago, a group of fisheries ecologists and managers examined the state of recreational fisheries in Canada and described some alarming patterns (Post et al., 2002, reprinted in Saas et al., 2014). Several high-profile Canadian freshwater recreational fisheries showed evidence of decline, risking erosion of their substantial economic and cultural value. Our overview of Canadian recreational fisheries of the day outlined several reasons they were susceptible to collapse, and that this outcome was apparently largely invisible. This essay will attempt three things: (1) set the stage by asking where we were in 2002 (and some story-telling as maybe I can get away with now), (2) outline the big advances in the field over the past 20+ years, for which the paper may have had some influence, and—maybe most importantly—(3) suggest some ideas of how we can advance recreational fisheries management as our paper fades into history. The essay is a personal perspective on development of the original paper, key scientific advances since its publication, a few ideas of what is next, and also some advice to early career fishery professionals setting out to make a difference. The reference list is short and represents my view of the most effective approaches for continued innovation in recreational fisheries science and management.
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 | 0.043 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.018 | 0.039 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".