Bibliographic record
Abstract
This work aims to develop interventions to reduce automobile idling, where a driver runs the engine unnecessarily while not moving. Idling is a serious problem that wastes fuel, pollutes the air, and releases greenhouse gas emissions. Drivers idle for different reasons, including misconceptions about the time needed to warm up their engines and how much additional fuel is expended by turning the engine off and back on. Information-based interventions, i.e., messages to address idling, may therefore work more effectively to change behavior by correcting such misconceptions than for other types of pro-environmental behaviors where corresponding misconceptions may not exist. This work incorporates Regulatory Focus Theory, a social-psychological framework which differentiates between promotion- and prevention-focused individuals. Furthermore, messages are framed with respect to idling-relevant concerns that participants identify — finance, health, or the environment. Participants were asked to express behavioral intention and engagement in response to messages tailored for their regulatory focus and domain of concern. Results revealed that 1) participants prioritized finance and health much more often than the environment; 2) most participant categories responded well to their targeted messages; 3) Promotion/Finance participants seemed especially challenging to motivate, but modifications to their targeted messages led to improved results.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".