The intersection of shark research, policy and the public: a bibliometric and altmetric view
Bibliographic record
Abstract
Sharks have traditionally been portrayed as dangerous animals by modern media, contributing to a negative perception in the public eye. On one hand, despite some species being listed as critically endangered, news about the perceived risk of sharks for humans protrudes more than other topics. On the other hand, conservation topics tend to focus on specific topics, such as finning, highlighting the divergence between scientific and mediatic discourses about sharks. Our research compares the attention of shark research topics across citations, tweets, news and policy mention to assess the salience of specific themes. We find that citations are evenly distributed across research communities, tweets and policy mentions exhibit a significant focus on conservation, and news mentions tend to focus on more sensationalist topics such as shark attacks or the repercussions of fisheries on coral reefs.
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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.133 | 0.204 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".