Understanding the mechanisms of administrative burden through a within-case study of Medicaid expansion implementation
Bibliographic record
Abstract
The importance of the administrative burden problem in public programs has been apparent during the COVID-19 crisis in the United States as millions of newly unemployed people have had to wait for unemployment checks and public health insurance benefits due to paperwork requirements, agency staff shortages, and outdated information technology systems. The resulting burdens have extended financial hardship, caused the coronavirus to spread, and eroded citizen and agency morale. Administrative burdens have long been known to be costly, yet remain fixtures of public benefit programs across the world. To reduce them, we need to understand their mechanisms. Formal policy solutions per se will not reduce administrative burdens because they do not exist solely by design. This article contributes to behavioral public administration by providing a comprehensive, empirical-driven theoretical framework for understanding the complex processes through which supply-side administrative burdens are instituted, modified, and eliminated. Using a retrospective within-case study method that utilizes participant observation, documentation, and archival records, the article traces the process by which a state eliminated administrative burdens in the process of implementing an initially straightforward expansion of Medicaid eligibility, thereby creating a model for simplifying and streamlining enrollment that was incorporated into the Affordable Care Act.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".