How Do Institutional Forces Promote Social Actions in Life-Threatening Events?
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".