Measuring Administrative Burden: Bringing the State “Back in” as a Reflexive Actor in Burden Reduction
Bibliographic record
Abstract
ABSTRACT This study examines how governments measure administrative burdens in citizen–state interactions. Although scholarly interest in the burden framework has grown, little is known about how states themselves track and reduce these costs. A scoping review of 38 academic and gray sources, complemented by interviews with 11 experts, identifies six measurement approaches currently in use. An analysis of their indicators and data shows that all six capture burdens only partially: none encompasses all four dimensions—time, money, effort, and psychological—and none integrates both subjective and objective data for each. These tools reflect narrow, fragmented understandings of what burdens are and how they are experienced, highlighting the need for stronger alignment between conceptual advances, measurement practices, and policy efforts. Drawing on our findings, we propose three policy recommendations to enhance burden measurement and outline three research directions to further the study of how governments monitor, interpret, and mitigate the burdens they produce.
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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 | 0.129 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".