A Call for Mini-Reviews: An Effective but Underutilized Method of Synthesizing Knowledge to Inform and Direct Fisheries Management, Policy, and Research
Bibliographic record
Abstract
Abstract Remaining current on emerging research in fisheries science is challenging. While review articles are often a go-to resource for managers and researchers alike, reviews in certain fisheries science subdisciplines are either dated or simply do not exist. Although there are a number of journals that publish lengthy reviews on topics relevant to fisheries, these are not always accessible and may not be read by managers, policymakers, and legislators. To address these concerns, there is a need for direct, concise, and timely review articles that tackle emerging issues (i.e., mini-reviews). Reviews of this type are rarely published in American Fisheries Society journals or fisheries journals in general, despite the fact that they have been widely successful and influential (in terms of both academic measures of research “impact” and in affecting change in management and policy) in ecological and conservation journals. We provide suggestions for developing high-quality mini-reviews and propose that Fisheries is an ideal outlet for these short and timely articles aimed at reaching a broad, multidisciplinary audience, including scientists, managers, policymakers, legislators, and other stakeholders. Resumen Mini-artículos de revisión: un método efectivo pero subutilizado para sintetizar el conocimiento e informar y dirigir el manejo, la política y la investigación en pesquerías La vigencia del conocimiento de la nueva investigación en pesquerías representa un desafío. A pesar de que los artículos de revisión son una fuente obligada tanto para los manejadores como para los investigadores, en ciertas sub-disciplinas de la ciencia pesquera las revisiones son obsoletas o simplemente no existen. Si bien hay varias revistas que publican extensas revisiones en tópicos relevantes para las pesquerías, éstos no siempre son accesibles y pueden pasar desapercibidos por los manejadores, políticos y legisladores. Para atender estos problemas, existe la necesidad de producir artículos de revisión directos, concisos y oportunos que aborden temas emergentes (i.e. mini-artículos de revisión). Las revisiones de este tipo son rara vez publicadas por las revistas de la Sociedad Americana de Pesquerías pese a que han sido muy exitosas y trascendentes (en términos tanto de medida académica del impacto de la investigación como en la afectación en cuanto a cambios en el manejo y la política pesquera) en las revistas de ecología y conservación. Se hacen sugerencias para desarrollar mini-artículos de revisión de alta calidad y se propone la revista Fisheries como un sustrato ideal para este tipo de artículos cortos y oportunos, dirigidos a audiencias amplias y multidisciplinarias que incluyen científicos, manejadores, políticos, legisladores y otros interesados.
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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 | 0.550 | 0.786 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.041 | 0.028 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.038 | 0.033 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.026 | 0.025 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".