Irregular stimulus distribution increases the negative footprint illusion
Why this work is in the frame
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Bibliographic record
Abstract
As a climate change mitigation strategy, environmentally certified 'green' buildings with low carbon footprints are becoming more prevalent in the world. An interesting psychological question is how people perceive the carbon footprint of these buildings given their spatial distributions in a given community. Here we examine whether regular distribution (i.e., buildings organized in a block) or irregular distribution (i.e., buildings randomly distributed) influences people's perception of the carbon footprint of the communities. We first replicated the negative footprint illusion, the tendency to estimate a lower carbon footprint of a combined group of environmentally certified green buildings and ordinary conventional buildings, than the carbon footprint of the conventional buildings alone. Importantly, we found that irregular distribution of the buildings increased the magnitude of the negative footprint illusion. Potential applied implications for urban planning of green buildings are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it