The Role of the Passive Voice Mindset in Regulating Healthy Eating
Bibliographic record
Abstract
Talking about eating in the passive, as opposed to the active voice, (e.g., The cake will be eaten vs. I will eat the cake) can lead people to see the act of eating to be triggered by the food to a greater extent, leading to the continuation of past eating habits. Depending on whether or not the past habits are healthy, the motivation for healthy eating may change as a result. In study 1, writing passive sentences increased the motivation for healthy eating to the extent that people reported eating healthy in the past. Moreover, in study 2 across 127 languages spoken in 94 countries, when the acted-upons of actions (e.g., the food in the act of eating) became relatively more salient in a language, people became more likely to act on cultural habits that may be relatively healthier, decreasing unhealthy eating. The results are important for understanding the perceived role of food in starting eating as it impacts healthy eating across cultures.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".