Prediction of Depot-Based Specialty Recycling Behavior Using an Extended Theory of Planned Behavior
Bibliographic record
Abstract
Curbside recycling has been a very successful neighborhood-level intervention designed to maximize waste containment, but many communities have specific limitations on what products can be recycled within their community bins and must rely on depots for recycling these specialty items. The purpose of this study was to examine an extended theory of planned behavior (TPB) that included both affective and instrumental attitudes and a planning construct to predict depot specialty recycling in a community sample across 1 month. Participants were 176 residents of detached homes who completed baseline measures of TPB and self-reported behavior 1 month later. Structural equation modeling identified a modest fit of the TPB, and 48% variance of depot recycling behavior was explained with the constructs of intention, planning, and perceived behavioral control, yet these constructs did not perform as well in predicting change in behavior across 4 weeks. Although proximity to the recycling depot did not relate to behavior, it significantly moderated the planning–recycling behavior relationship, whereby those who lived closer to the depot had larger planning–behavior relations than those who lived further away. Developing plans to recycle may help in addition to motivation, but these are still contingent on there being an easy commuting distance to a depot.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".