Where has all the recruitment research gone, long time passing?
Bibliographic record
Abstract
Abstract For most of the past 100 years, research into recruitment processes—as pioneered by Johan Hjort—has been a consistent focus of research in fisheries science. This was reflected not only in the literature but in the organizational structures and research strategies of national and international fisheries research and management institutions. Over the past decade or so, we perceived that recruitment research is fading, if not into obscurity then at least into a more marginal place in fisheries and marine research. In this paper, we assess if our perception is real by quantifying trends in scientific publications and in the work activities within ICES during specific periods extending back to the 1920s. Our analysis documents a decline in research on recruitment processes. We put forward three possible hypotheses to explain this decline: 1. All the key research questions about recruitment have been answered; 2. The volume of research on recruitment processes has declined because the answers are no longer relevant; 3. Recruitment research has been co-opted by more trendy, possibly ephemeral, and research topics. There is little evidence to support the first two of these hypotheses and we consider the third to be the most plausible. Finally, we conclude that this new terminology/repackaging of recruitment research does not bring with it new and fresh thinking and, therefore, comes at a cost that should be carefully considered.
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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 | 0.079 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".