Bibliographic record
Abstract
Abstract Resource-consumption systems can be defined by a resource inlet, a control volume where the resource is used, and a waste outlet. Specific to water, many existing conservation strategies focus on reducing the in-flow of water into a control volume. Instead, this work explores reducing waste out-flow, which causes accumulation in the control volume. This strategy aims to motivate users to reduce resource in-flow in response to accumulation in the control volume, and thus modify behavior. To test this strategy, Amazon Mechanical Turk workers completed three randomly ordered handwashing simulations with different sink-outflow rates online. Study participants (N = 74) significantly reduced consumption of water when it accumulated quickly in the sink (p < 0.0001). Participants reduced water consumption, on average by 14% at lower outflow rates, as they decreased inflow rates to prevent sink overflow. Many pro-environmental behavior interventions are limited in their reliance on user motivation and intention to reduce resource consumption. In contrast, the reduced-outflow intervention significantly reduced water usage (p < 0.001) of individuals, regardless of self-reported daily pro-environmental behavior. This result suggests that the developed intervention relies less on user intention. Overall results support that reducing outflow can increase sustainable user behavior when properly executed. In-person testing is discussed 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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".