Bibliographic record
Abstract
Why should, and how can, the fields of climate science and geroscience (which studies the biology of ageing) facilitate the cross-disciplinary collaboration needed to ensure that human and planetary health are both promoted in the future of an older, and warmer, world? Appealing to the ideal of ‘wisdom-oriented’ science (Maxwell 1984 In From knowledge to wisdom: a revolution in the aims and methods of science ), where scientists consider themselves to be artisans working for the public good, a number of the real-world epistemic constraints on the scientific enterprise are identified. These include communicative frames that stoke intergenerational conflict (rather than solidarity) and treat the ends of planetary and human health as independent ‘sacred values’ (Tetlock 2003 Trends Cogn. Sci. 7 , 320–324) rather than as interdependent ends. To foster ‘climate geroscience’—the field of knowledge and translational science at the intersection of climate science and geroscience—researchers in both fields are encouraged to think of novel ways they could make researchers from the other field ‘conversationally’ present when framing the aspirations of their respective fields, applying for grant funding and designing their conferences and managing their scientific journals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".