The Social and Psychological Foundations of Climate Solutions
Bibliographic record
Abstract
"The debate over climate change has come largely from the physical sciences in defining the problem, and from one narrow branch of social science neoclassical economics in generating solutions. While this focus helps to define and address issues related to what is at stake and what to do about it, a greater and more varied voice from the social sciences (e.g., sociology, psychology, anthropology, political science) is needed to address issues related to how the problem is viewed by the public and how that public will respond to the solutions that are imposed upon it. In the eyes of the social scientist, people employ ideological filters when analyzing important issues. These filters are influenced by their identity and worldview; that is, their belief systems. Critical to the formation of such belief systems are the groups to which people belong and the biases and values of the individual. Unfortunately, these cultural and psychological dimensions are overlooked because social scientists that can identify and analyze them have been notably absent from the public debate. This omission is due both to a lack of awareness among policymakers of the valuable insights that the broader social sciences can offer and to the internal reward and incentive systems of the academy that bias social scientists away from engaging in public debates. This article discusses how the other social sciences could augment the proposed economic solutions to greenhouse mitigation with research on perception, decisions, consensus, and action across three levels of analysis: the individual, organizational, and institutional levels. It also discusses a series of proposed interventions to overcome the filters and biases that take place at these levels."
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".