Bibliographic record
Abstract
A user fee is a fee assessed by the government when it provides a good or service to a private party. Classic examples include the fee to enter Yellowstone National Park and payment to the U.S. Postal Service for a first-class postage stamp. In these transactions, the government provides a specific benefit to an identifiable recipient, who pays the fee. Although the federal government has assessed user fees since this country's founding, broad authorizing legislation in the middle of the 20th century made it possible for agencies to craft user fee programs on their own initiative. Interest in relying on user fees rather than general revenue (taxes) intensified in the final quarter of the century as the federal courts sorted through issues relating to the constitutionality of user fees and concerns mounted about the growing federal debt. Today, hundreds of user fees are in place across dozens of agencies, and collections exceed more than $500 billion per year. Congress has now supplemented the broad mid-century authorizing legislation with statutes that authorize or require specific agencies to collect user fees. Some enactments are prescriptive (even as to the amount collected), while others specify a formula or objective (such as full cost recovery). Some statutory authorizations are permanent, while others are slated to expire, so continuing to collect fees requires reauthorization. Some agencies are fully funded by user fees, some agencies have discrete programs that are fully funded by user fees, and some agencies receive only modest support from user fees. Some agencies collect more money from fees in a year than they will use that year and maintain the leftover funds in a reserve fund in the event of a shortfall, while others do not. And so on. This Article examines the design and implementation of user fee programs across the Executive Branch. Design means design both at the congressional level (i.e., the drafting of statutory user fee authority) and at the agency level (i.e., the implementing of statutory user fee authority). This Article discusses design decisions that must be made and the choices available; identifies practical and legal constraints on the choices available; explores the relationship between these choices and core values of administrative law; and explains how program designers might make these choices, as a procedural matter and as a substantive matter. On the basis of this discussion and informed by a series of stakeholder interviews, this Article offers recommendations for agencies and lawmakers relating to user fee design and implementation.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.309 | 0.180 |
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".