Religious Affiliation and Wrongdoing: Evidence from U.S. Nursing Homes
Bibliographic record
Abstract
We explore the relationship between organizational religious affiliation and wrongdoing using a unique data set on inspections in 16,101 nursing homes over five years. We find that violations of standards of care are more severe in religiously affiliated homes. We track this difference to a reduction in the likelihood that organizational members file complaints rather than poorer behaving caretakers or differential treatment by enforcement agents. Fewer complaints increase the time that religiously affiliated homes operate without monitoring, which allows violations to escalate before they are detected. Our findings highlight an understudied process in the literature on organizational wrongdoing: Although much attention has been devoted to how inspector bias can lead to incorrect conclusions about the true rates of wrongdoing across organizations, religious affiliation can lead to similarly incorrect conclusions—but through an internal organizational process. This paper was accepted by Lamar Pierce, organizations. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4350 .
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".