Editorial: Highlights in environmental psychology: pro-environmental purchase intent
Bibliographic record
Abstract
Pro-environmental purchase is a topic of rising importance worldwide because it contributes to making consumption patterns more responsible (De Canio et al., 2021). Pro-environmental behavior can be defined as “behavior that harms the environment as little as possible, or even benefits the environment” (Steg and Vlek, 2009, p. 309; Ertz et al., 2016, p. 3971). Consequently, pro-environmental purchase (PEP) must be understood as a specific form of buying that harms the natural environment as little as possible and even benefits it. Products and services falling under that category are also called “green” and include, among others, energy-efficient household appliances (Nguyen et al., 2016; \nTeoh et al.), water-saving appliances (Wang and Tian), eco-tourism (Fennell, 2014), ecofriendly clothing (Wiederhold and Martinez, 2018), eco-designed products (Zeng et al., 2017), bioplastics-based products (Atiwesh et al., 2021), organic food products (Rodier et al., 2017), or products and services facilitating pro-environmental behaviors such as compost bags, for example. A key factor in this is consumers, who are a fundamental part of the overall consumption process and consumer society, and it is crucial to better investigate what drives them to pro-environmental purchases.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".