Turning Tides: Sustainability Measures for Shark Conservation
Bibliographic record
Abstract
This major project paper explores the critical role of environmental documentary films in addressing the lack of ocean-related environmental education programs in academic and non-academic settings. Focusing on marine conservation and biodiversity, particularly sharks, the study showcases how sharing scientific knowledge through documentary films facilitates easy and comfortable engagement with ocean-related topics. By targeting youth, who are well-acquainted with technology and media, the research emphasizes the potential of documentary films to improve environmental knowledge retention, thus advancing ocean literacy\nand awareness.\nThe study incorporates in-person observations of at-risk marine environments, such as coral reefs and seagrass meadows, as well as endangered marine species, primarily sharks and rays, to assess specific conservation needs and understand the correlation between ocean and human health. Through visual documentation, the film presents compelling evidence of global ocean health decline, urging governments, policymakers, and the public to prioritize socio-political changes. Among the evidence presented, an interview with marine biologist and shark scientist David McGuire offers valuable support and credibility to the information and examples being shared.\nAligned with the United Nations Sustainable Development Goal #14, the research shares the current state of oceans and their biodiversity. By bridging science, policy, and education through film, it contributes to increasing ocean literacy and inspires conservation behavior. The paper concludes by affirming the effectiveness of film and media as educational tools, breaking language barriers and providing universally understandable evidence that encourages future efforts to promote ocean stewardship.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".