Bibliographic record
Abstract
The purpose of this study aimed to examine international trade network of Fishery Commodities. In order to investigate the target network, the social network analysis method was adopted by using NetMiner V.4 software tool. Among OECD members, this paper explores these country's roles associated with influence analysis in fishery commodities trade network. Network centrality showed the most import countries are France and Germany. In the case of export network, the most significant countries are United States, Norway, France, Spain, Germany, and Japan in order, when computed by degree and closeness centrality. When calculated by betweenness centrality, however, there are important countries in order such as France, Germany, Netherlands, Italy, and United Kingdom, except Japan. Regarding import network, the most import countries are Norway, United States, Japan, Spain, France, Sweden and Germany in order, computed by degree and closeness centrality. Betweenness centrality, however, are greater in such countries as France, Germany, United Kingdom, Italy and Canada in order, except Japan. From this findings, the paper showed Japan has not been able to perform an intermediary role in international fishery commodities trade network. Similar to Japan's situation, Korea's role is not significant as well.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".