Psychology's contributions to understanding and addressing global climate change.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global climate change poses one of the greatest challenges facing humanity in this century. This article, which introduces the American Psychologist special issue on global climate change, follows from the report of the American Psychological Association Task Force on the Interface Between Psychology and Global Climate Change. In this article, we place psychological dimensions of climate change within the broader context of human dimensions of climate change by addressing (a) human causes of, consequences of, and responses (adaptation and mitigation) to climate change and (b) the links between these aspects of climate change and cognitive, affective, motivational, interpersonal, and organizational responses and processes. Characteristics of psychology that cross content domains and that make the field well suited for providing an understanding of climate change and addressing its challenges are highlighted. We also consider ethical imperatives for psychologists' involvement and provide suggestions for ways to increase psychologists' contribution to the science of climate change.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it