The Perils of Explaining Climate Inaction in Terms of Psychological Barriers
Bibliographic record
Abstract
Abstract As awareness of climate change and its consequences increases, many have asked, “Why aren't people taking action?” Some psychologists have provided an answer that we describe as a “psychological barriers explanation” (PBE). The PBE suggests that human nature is limited in ways that create psychological barriers to taking action on climate change. Taking a critical social psychology approach (e.g., Adams, 2014), we offer a critique of the PBE, arguing that locating the causes of inaction at the psychological level promotes a misrepresentation of human nature as static and disconnected from context. Barriers to environmental action certainly exist, and most if not all involve psychological processes. However, locating the barrier itself at the psychological level ignores the complex interplay between psychological tendencies, social relations, and social structures. We consider the ways in which psychological responses to climate change are contingent upon social‐structural context, with particular attention to the ways unequal distributions of power have allowed elites to block climate action, in part by using their power to influence societal beliefs and norms. In conclusion, we suggest that psychologists interested in climate (in)action expand their scope beyond individual consumer behaviors to include psychological questions that challenge existing power relations and raise the possibility of transformative social 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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".