Recreational shark fishing in Florida: How research and strategic science communication helped to change policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sharks are taxa of significant conservation concern, and while commercial overfishing is the leading cause of population declines, recreational angling poses an increasing threat to some coastal shark populations. Here, I present a detailed case study of my role in a multi‐stakeholder process to improve policy surrounding recreational fishing for threatened sharks in Florida. While many other people including other scientists, concerned citizens, responsible conservation‐minded anglers, and environmental activists played key roles throughout this process, my scientific research and public engagement contributed significantly, and is the focus of this case study. Over the course of several years, my research documented the scope of several unnecessary angler practices that were harmful to threatened shark species. As a result of my research and stakeholder interactions, I was able to propose science‐based politically feasible policy solutions, and I strategically communicated the problem and possible solutions to policymakers, journalists, environmental activists, scientific professional societies, and the public. In July of 2019, the Florida Fish and Wildlife Conservation Commission enacted new rules for land‐based shark fishing in Florida waters, incorporating several of my proposed solutions. This case study demonstrates that through careful planning, understanding policy, developing a strategic communication plan, and networking with key stakeholders, even early career researchers can successfully help to change policy and help protect threatened species. Supplementary materials (Data S1) contain detailed background information, a timeline of events, and a diverse set of examples of my science communication.
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.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it