Ecological grief and anxiety: the start of a healthy response to climate change?
Bibliographic record
Abstract
There is increasing global awareness that the next 10 years must be a period of extensive and rapid mitigation and adaptation to safeguard humanity from the worst harms of the climate crisis. An urgent need for action was recently underscored by three Intergovernmental Panel on Climate Change Special Reports: the Special Report on Global Warming of 1·5°C, the Special Report on Climate Change and Land, and the Special Report on the Ocean and Cryosphere in a Changing Climate. Similarly, the 2019 report of the Lancet Countdown on Health and Climate Change1 highlighted potentially catastrophic health risks for a child born today if an adequate response to climate change does not occur, including increased rates of food insecurity and undernutrition, of diarrhoeal and infectious diseases, and of complications from air pollution, and increased morbidity and mortality from exposure to extreme weather events (eg, heatwaves, flooding, wildfires, and hurricanes).
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".