Bibliographic record
Abstract
Abstract A novel intervention to increase water-conserving behavior was developed and tested. Behavior-change interventions range from information-based, where individuals have full control over whether they act on the provided information, to forcing/automation, where individuals have no control over the desired behavior. This study’s intervention was devised to be more forceful than providing information alone, but unlike forcing/automation, still allows individuals to control whether they perform the desired behavior. While resource-conservation strategies tend to target resource intake, the studied intervention examines whether limiting resource waste outflow can also limit resource intake. Specific to water, this study explored whether reducing wastewater outflow, causing accumulation, can reduce water inflow. Data were collected online using simulations of handwashing at a sink, which had different sink-outflow rates. Amazon Mechanical Turk workers completed three randomly ordered handwashing simulations. Study participants (n = 72) significantly reduced simulated consumption of water when it accumulated quickly in the sink (p < 0.001). Participants reduced simulated water consumption, on average by 14% at lower outflow rates, as they decreased inflow rates to prevent sink overflow. In contrast to informational interventions that rely on user motivation, reducing outflow significantly decreased simulated water usage, independent of participant-reported performance of other pro-environmental behaviors. Thus, reducing outflow may be effective regardless of individuals’ motivation to act sustainably. Also discussed is the value of online simulations to test pro-environmental behavior interventions. Finally, limitations and next steps, including in-person testing, are outlined as future work.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".