Bibliographic record
Abstract
Art is a powerful bridge to foster empathy, promote dialogue, and challenge societies' destruction of nature. The creation of a series of oil paintings on lithium extraction was to highlight the destruction of the environment in one of the driest areas of the planet, pumping brine from hypersaline lakes (salars) for evaporation and using scarce water resources for processing, highlighting the devasting impact on Indigenous communities, and ecosystems. Alternative solutions proposed by the Indigenous communities, which can preserve water resources, can create a more equitable future that is less damaging to the environment. Knowledge and understanding were co-created through the invaluable review process, a journey in which each of us played a crucial role in shaping the narrative. The co-constructed formats helped strengthen the need to protect the planet and the rights of marginalized communities. Art transcends cultural barriers, has the potential to foster shared understanding, and can inspire optimism and encouragement for a future of peace and justice. By engaging with the artworks, the conference community played a pivotal role in understanding the wishes of Indigenous communities and their proposed solutions for a more equitable future. (communication by email on 2025-04-29)
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 | 0.094 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.052 | 0.038 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".