Diving into SDG 14, Life Below Water: A VR Experience for Deeper Understanding
Bibliographic record
Abstract
The United Nations' Sustainable Development Goal (SDG) 14: Life Below Water aims to conserve and sustainably use the oceans, seas, and marine resources for sustainable development. However, the complexity and vastness of the ocean can make it difficult for students to fully understand and care about marine conservation and the sustainable use of ocean resources. In this session, we propose using FrameVR to design an immersive learning environment that promotes deeper engagement with SDG 14 by providing students with an interactive opportunity to explore the current state of the ocean and the effects of human activities on marine life. One of the key features is integrating real-world data from carefully curated and reputable educational media to ground the learning, including infographics, Tik Tok and YouTube videos, images, audio lessons, personal stories, articles, and learning games. A social component allows students to work together in developing solutions to the challenges they encounter in the FrameVR experience. By creating an environment that will enable them to explore, learn, and collaborate with peers, we aim to enhance student awareness, engagement, empathy, and motivation to take action on SDG 14.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".