Environmental psychology in the Philippines: Growth, challenges and prospects
Bibliographic record
Abstract
Environmental psychology is a subdiscipline of psychology that deals with the interplay between people and their environment (Stern, 2000), with a huge part of the discipline dedicated to conservation and sustainability (Giuliani & Scopelliti, 2009).The discipline has grown rapidly over the past decades making its impact felt in terms of research, interventions and policies.However, environmental problems are global problems that entail scientific attention in all parts of the world; but studies in the field have been largely focused on the Global North or Western and rich countries.Tam and Milfont (2020) reviewed the articles that were published in the Journal of Environmental Psychology, the flagship journal of the field, between 1981 and 2019 and found that most single-country and multiple-country articles were from Western and developed countries, including the USA, UK, Canada and the Netherlands, among others.Environmental psychology can benefit from a balanced and complementing knowledge base between the Global North and the Global South.The predominant focus on Western knowledge could create blindspots in understanding the nuanced antecedents of environmental actions and the processes in which they operate.Emerging research argues that assumptions based on Western contexts do not always operate similarly in non-Western settings (Eom et al., 2019).For instance, climate anxiety was identified by Western studies as a precursor of climate action (Whitmarsh et al., 2022).However, recent evidence based on 32 countries showed that climate anxiety was associated with climate action and pro-environmental behaviours in individualistic or Global North nations but not in collectivistic nations or Global South countries (Ogunbode et al., 2022).Growing evidence provides cues on the importance of other-oriented motivations as a precursor of pro-environmentalism in the Global
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".