Are Citizens Responsive to the Regulatory State? The Effect of Regulation on Evaluations of Early Childhood Education and Care ( <scp>ECEC</scp> )
Bibliographic record
Abstract
ABSTRACT Public service delivery has increasingly involved mixed markets, with for‐profit, not‐for‐profit, and government‐delivered programs. In such contexts, regulation can protect the public interest by enhancing safety, expanding consumer choice, or improving the quality of goods or services. In this article, we explore how citizens experience varying regulated markets, and whether regulatory stringency shapes citizen perceptions of service quality in the context of early childhood education and care (ECEC) services in the United States. We rely on automated textual analysis of online Google reviews of ECEC alongside a dataset of state policy stringency that tracks whether states allow for unlicensed care environments. Using a regression discontinuity design to test the impact of regulatory systems on reviews of care, we find evidence that parents in states with less stringent regulations are more likely to post negative reviews and express anger and anxiety, relative to parents in states with robust regulatory regimes.
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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 | 0.023 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".