Real research or sham science? A review of Japan’s scientific whaling
Bibliographic record
Abstract
Centuries of unregulated hunting lead to the decimation of whale populations globally. A moratorium on whaling allowed some stocks to start recovering, but others are not as promising. The Japanese lethal research on whales is permitted under the International Whaling Commission’s regulations allowing for scientific sampling of cetaceans, despite the 1982 moratorium on whaling. However, many in the scientific community suggest that the Japanese research is really a front for commercial whaling. The research programs in both the Antarctic and North Pacific (JARPA and JARPN) are not meeting their objectives and non-lethal techniques would be more effective. The Japanese government’s agenda at the IWC is to restart commercial whaling and appears to be actively promoting the consumption of whale meat from the research vessels. Japans internal market is not properly regulated and meat packaged for consumption has been found with pathogens and extremely high levels of toxins and heavy metals. Genetic analysis has indicated whale meat in markets contains internationally protected species, as well as non-whale tissues. Due to the extreme deficiency in our knowledge of global cetacean populations and the lack of infrastructure to monitor and enforce quotas, whale conservation should take priority over premature harvesting or unscientific research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".