What makes some fisheries references highly cited?
Bibliographic record
Abstract
Abstract We identify the 199 most‐cited fisheries references up to July 2014, topped by Nelson's Fishes of the World. Few book chapters, databases or reports were included, but review articles and field guides were over‐represented. Publishing in Science, Nature or Proceedings of the National Academy of Sciences USA is associated with a 34‐fold increase in the probability of an article being highly cited, but many highly cited references were also published in journals with impact factors under eight. Proportional contributions to references, taking into account number of authors, author order and other key factors, revealed Bill Ricker and John Roland Brett as the greatest individual contributors, and the US, Canada and the UK as greatest country contributors, with Canada significantly over‐represented. Female representation on the list was historically low before increasing to 21% in the 1990s, and reflected gender changes in the field of fisheries. When compared to >2000 control papers published in the same journal and year, highly cited fisheries papers were significantly longer (20.4 vs. 9.8 pages) and had more authors (5.8 vs. 4.3), references (118 vs. 51), tables and total illustrations; these differences were greater when high‐profile general journals were excluded, but lower when calculated on a per‐page basis. References with more than six authors jumped from 0 to 27% in 2000, coinciding with a rapid uptake of email among fisheries scientists. Overall, we find no shortcut to publishing highly cited references: they require substantial time, effort and knowledge invested in new hypotheses, textbooks, field guides, new discoveries, broad meta‐analyses, new methods and reviews.
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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 | 0.005 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".