Bibliographic record
Abstract
The intensifying warming of the planet over the past several decades is a manifestation of centuries of uneven and inequitable extractive economies. This warming is well known to be the main force driving shifts in climatological conditions and extreme weather events leading to increasingly severe impacts on planetary systems. Every year, more locations on earth are experiencing heat waves, intense droughts, longer and larger fire seasons, increased tropical storm intensity, and sea level rise at rates that would have been unthinkable a generation ago while near daily news reports document the increasing toll that this changing climate plays in exacerbating social and ecological vulnerabilities. Just this year, at the start of the Northern Hemisphere summer of 2023, a massive tropical cyclone has killed over 145 people in Bangladesh and Myanmar, western Canada has already seen as much forest burned in a few days as it does in an entire summer, drastically diminishing air quality over half a continent, the Po River Valley in Italy has been ravaged by floods after experiencing two years of extreme drought, and California has experienced deadly and pervasive atmospheric rivers after years of record-setting fire seasons and water shortages. In this special issue, rather than prioritizing benign and depoliticized notions of adaptive capacity and resilience, as is far too common within mainstream discussions of climate change, we highlight the theme of flood and fire to examine these events as compounding contemporary crises and responses to phenomena that are devastating, transforming, and reformulating communities, ecologies, and governing processes around the planet.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".