Bibliographic record
Abstract
Although considering questions of wisdom and humility in relation to climate change may seem like a soft theoretical topic for an issue that needs hard and clear responses, there is an increasing literature that suggests the global inability to agree on the nature of climate change and what a response entails may be grounded in modern ways of thinking and being—ways that have long marginalized such inquiries. The increasing power of science and computer modeling has not only give us new ways of thinking about our relation to climate, but also represents our entry into a new sense of being where we are aware of humanity's interconnection with an ever‐changing climate system. Research with indigenous views of climate change and paleoclimatology are revealing historic insights on the evolving relation between the human mind and cycles of climate change. While such research can help situate our present realizations about human embeddedness within a longer history of human–climate relations, it is also necessary to recognize the unique dimensions of our current situation. This includes the increasing power of industrial humanity to destabilize global climate–human systems through greenhouse gas emissions; to technologically monitor those changes; and to promote changes in behavior that can be ethically responsive to global, not simply regional, climate–human relations. This article considers climate change as a challenge to modern ways of thinking and being, and concludes by contemplating some implications of returning to an older humility and wisdom.WIREs Clim Change2014, 5:247–260. doi: 10.1002/wcc.267 This article is categorized under: Social Status of Climate Change Knowledge > Sociology/Anthropology of Climate Knowledge Trans‐Disciplinary Perspectives > Humanities and the Creative Arts
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".