Bibliographic record
Abstract
How are climate change, weather-related disasters, food and water insecurity, and energetic and infrastructural collapse narrated audiovisually in the most environmentally vulnerable areas of the Planet? This book addresses this and related questions by adopting a local and transdisciplinary perspective on river deltas from different areas of the world. River deltas have historically been hotspots for human civilizations, as populations settled in their fertile grounds seeking resources and opportunities for prosperity. Despite this, the terrains and livelihoods of those who rely on them are under threat from human exploitation, environmental degradation, and rapidly accelerating climate change. Inspired by the UN Sustainable Development Goals, this book provides a range of focused audiovisual analyses of deltaic spaces. Ranging across a variety of media, including documentary filmmaking, animation, photography, collaborative comic making, participatory visual art practices, soundwalking, and film analysis, it examines the role that contemporary audiovisual media play in forging global environmental imaginaries. In doing so, it adopts a transdisciplinary approach to the Blue Humanities from countries across the world, including Canada, Bolivia, Brazil, Greece, Nigeria, Senegal, India, Bangladesh, Myanmar, Thailand, and Vietnam.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.376 | 0.138 |
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".