Environmental Psychology and Sustainable Development: Expansion, Maturation, and Challenges
Bibliographic record
Abstract
In this summary article, some advances of, the potential for, and challenges faced by environmental psychology as a contributor to sustainability science are outlined. In its first 40 years, it has evolved from a discipline primarily—but never solely—concerned with proximate architecture to one that adds concern with larger‐scale issues, particularly sustainability. This growth of interest has in turn led to increased interest within it in public policy, technology, cooperation with other disciplines, multilevel analyses of problems, the ingestion of new ideas, and concern with the health of the biotic and ecological world. Some challenges are that the central proponents of “sustainability science” itself have not acknowledged environmental psychology as a potential contributor, the field is comparatively young, that it needs to explore biotic and ecological issues more, needs to help discriminate facts from nonfacts about environmental problems, and needs to warn sustainability science about the daunting task of overcoming environmental numbness and self‐interest in individuals. Nevertheless, there is hope: sustainability scientists, including environmental psychologists, may be Adam Smith's “invisible hand.”
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".