Bibliographic record
Abstract
Point-counterpoint dialogue creates an opportunity for sharing different perspectives on important issues in fisheries and aquatic sciences. These can take many forms including live “debate” style events that require skilled moderation. The American Fisheries Society (AFS) has a history of facilitating such sessions at AFS meetings going back to at least 1993. Then AFS President Ray Hubley included point-counterpoints in his program of work, which led to several sessions including at Division meetings. One such event at the North Central Division Meeting in 1993 focused on conservation genetics and current stocking practices where the participants explored the extent to which they were compatible. They published a summary of those discussions in Fisheries (see Philipp et al., 1993). Twenty years later the concept has languished. Yet today, there is no shortage of issues or topics for which different views are held by members of our profession. To that end, Fisheries will be launching a new series of articles that represent a written version of the point-counterpoint approach. We are open to different formats. For example, once a topic and two “teams” are identified, each would be tasked with writing a 3,000-word perspective presenting their view. The two teams then exchange their text and write an additional 2,000 words where they respond to the perspectives raised by the other team. These two pieces are published back-to-back in the journal after peer review (focused on clarity, civility, and factual correctness—by the same referees and handling editor for both papers). The last component of the process is bringing together the two teams to generate a short joint paper that highlights key messages and opportunities for addressing the issues or bridging the divide between the differing perspectives. Indeed, that was the initial approach that was embraced in Cooke et al. (2025, this issue) and Corsi et al. (2025b, this issue) for their papers on the extent to which individual outcomes matter in catch-and-release science and management. However, upon exchanging papers it was agreed that they stood alone well and that it was clear we could easily find middle ground in a summary paper (see Corsi et al., 2025a, this issue). The point is that we are flexible and keen to work with authors to structure these in ways that work for a given topic. The editorial team of Fisheries will be generating ideas and reaching out to potential authors. However, we also welcome members of our professional community to identify topics and reach out to the editorial team. Point-counterpoints can have an in-person (or online) component like back in 1993 followed up by a paper in Fisheries or use Fisheries as the forum for orchestrating the dialogue as described above.
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 | 0.074 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.042 |
| Scholarly communication | 0.034 | 0.035 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.067 | 0.052 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".