Atmosphere at Briefing Sessions and Its Influence on Local Residents’ Intention to Participate in Discussion
Bibliographic record
Abstract
Although it is widely recognized that atmosphere influences people’s decision‐making process, few studied have examined the effect of atmosphere in the context of consensus building concerning the construction of controversial infrastructures. At local residents’ briefing sessions, the negative words used by certain members of the strong opposition can often create a negative atmosphere, leading to unpleasant arguments. Therefore, in this study, a vignette experiment was conducted to examine the influence of the atmosphere at briefing sessions on local residents’ intention to participate in discussion. The results showed that local residents reported greater intention to participate in discussion in a positive atmosphere compared to a negative atmosphere. As for the cognitive process, however, while in the positive atmosphere only a single factor (i.e., interest) affected local residents’ intention, in the negative atmosphere multifactors (e.g., procedural justice, disbenefit, and mental burden) affected the intention. These findings suggest the importance of choosing an appropriate strategy to increase resident’s intention to participate in discussion depending on the atmosphere (positive or negative). The psychological mechanism of the influence of atmosphere and effective strategies that project implementers should take when the atmosphere becomes negative at a briefing session are discussed.
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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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.000 |
| 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".