The Effectiveness of 3-D Compared to 2-D Signage on Recycling Behaviour
Bibliographic record
Abstract
Using 3-D objects as examples, rather than 2-D icons on signs, to help people learn recycling categories has shown mixed results in observational studies, so an online experimental study was conducted to attempt to clarify the findings. The main hypothesis was that participants would perform faster and more accurately if they learned the recycling categories through images of 3-D objects rather than by 2-D icons. Furthermore, several exploratory hypotheses were suggested: Participants given both types of signage—3-D + 2-D—would perform better than the 3-D and 2-D conditions on their own, and subjective workload and user engagement would predict differences in performance between conditions. An ANOVA found no differences between any of the three conditions in terms of accuracy of sorting performance, subjective workload, or user engagement. However, the 3-D + 2-D condition demonstrated a significant, small-to-medium sized increase in sorting speed when compared to the other two conditions, suggesting that combined 3-D + 2-D signage speeds up decision making without negatively impacting accuracy. One possible explanation is that redundancy of information in the combined condition reduced uncertainty and led to increased speed. However, replication of this result is required because of some limitations inherent to the current study.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".