Reconceptualizing Administrative Burden Around Onerous Experiences
Bibliographic record
Abstract
ABSTRACT Despite ongoing discussions on the need to improve the conceptualization and measurement of administrative burden, several conceptual problems remain. This study offers the first systematic analysis and evaluation of this increasingly central public management concept. Using an ontological-semantic approach, I show that the current conceptualization fails to fully and directly account for individuals’ onerous experiences. I address five interrelated issues, including the overlap of cost categories and the conflation of state actions with onerous experiences. While psychological costs should be retained, I argue for abandoning the other cost categories. Building on previous reconceptualization efforts, I propose a new framework focused on time, money, effort, and psychological costs. Additionally, I explore the structure of the concept and propose specific indicators for each dimension. I then discuss the independence of these dimensions, their capacity to reflect the distributive nature of burdens, and avenues for empirical validation.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.018 |
| 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".