How Do Institutional Forces Promote Social Actions in Life-Threatening Events?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Life-threatening events endanger the survival of community members. During these critical times, service businesses that remain operational face increasingly challenging decisions, including whether to maintain regular operations or adapt their service to meet the community’s evolving needs. From the community’s perspective, such operation decisions may transcend mere business strategy and constitute social actions that serve the public interest. Based on employee scheduling of 19,265 restaurants and bars located in 1,773 U.S. counties, our study shows how regulatory institutional force (existing government small business policies), normative institutional force (civic network), and cultural-cognitive institutional force (cultural tightness) jointly affect these small service providers’ operation decisions regarding proactively reducing or maintaining their work time, at the early stage of the COVID-19 pandemic. In this context, reducing work time constitutes a social action that protects public health. The findings suggest that in culturally loose regions, civic network motivates small service providers to reduce work hours. In culturally tight regions with unfavorable small business policies, such a network leads to an increase in work time. Given the close ties between small businesses and local communities, understanding the role of institutional forces can help small service providers align their business strategies with local institutional dynamics during life-threatening events.
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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 | 0.001 |
| 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) | 0.000 | 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