Eficiencia productiva en la industria pesquera: un análisis bibliométrico (1979-2023).
Bibliographic record
Abstract
The literature on productive efficiency in the fishing industry is extensive and diverse. This study applies a bibliometric analysis to review 626 scientific articles based on the Scopus database from 1979 to 2023. The results show that, from 1979 to 2023, there was a significant increase in the number of publications. In the first years of the research (1979-1990), the preponderance of publications was concentrated in specific geographic areas such as the United States, Canada, the United Kingdom, Australia y Belgium (5 countries). Subsequently (1990-2023), and thanks to international collaboration that, to a certain extent, led to this change, the scope of Productive Efficiency in the fishing industry experienced a gradual expansion towards larger geographic regions, expanding from Asia to the from South America (76 countries). The results indicate that Aquaculture Economics And Management, Fisheries Research, Aquaculture, Marine Resource Economics and Marine Policy were the top 5 journals for publication during 1979-2023 for this field. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) have been the most used approaches in the research field in recent decades. In recent years, fishery-related studies in technical efficiency, economic efficiency, fishery management, optimization, fisheries economics, efficiency and sustainability have become increasingly interesting to researchers. The findings of this study offer a deeper understanding of publication trends, identify hotspots, and point to future research directions in this evolving field.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.091 | 0.164 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".