Commentary on "On the importance of the plumber: the intersection of theory and practice in policymaking for federal financial institutions"
Bibliographic record
Abstract
and delineating the reasons or justifications for the programs. In particular, were they established to serve a public or a special-interest benefit? The extent to which a given procedure is efficient depends on what the program is expected to achieve. For example, if a student-loan program is supposed to make it possible for poor students to attend colleges so that they can become wealthier than they otherwise would be, the interest rate charged might be a market rate. If this is the purpose of the program, an essential question is whether there is market failure—that is, why and to what extent do private sector lenders not offer such loans? Is there some legal or regulatory impediment that forecloses or restricts private sector lending? Is such lending insufficient because there is a positive (negative) externality that could effectively be achieved (alleviated) with a government program? If the purpose of the program is to benefit colleges, though, by allowing them to charge higher tuition to poor students rather than offer them scholarships and/or if the purpose is to help poor students become better educated in general because this benefits the nation, the interest rate should be below market rates for all poor students. An understanding of the reason for specific programs also is necessary to answer Elliott’s concerns and questions of how those programs should be administered. The “law of unintended consequences” plays a particularly important role here. An example is the bidding procedure for rights to the Federal Communications Commission (FCC) spectrum that Elliott discusses. D ouglas Elliott (2006) begins his discussion of some important issues concerning how federal government financing and insurance programs should be structured by assuming that these programs are here to stay. He writes (p. 260): “The federal government has a long history as a lender and insurer, and there is no sign that this is going to change. If anything, concerns about the federal budget deficit are likely to encourage an expansion of these programs.” He perceptively explains that “Lending and insurance programs allow politicians to throw out multibillion dollar figures for the volume of good their proposals will provide, without having the budget cost approach those levels. This is especially true if politicians use overly optimistic figures for the proportion of borrowers who will actually pay the loans back or the proportion of insureds who will submit claims.” Having presented both the fact of the programs and reasons why they are attractive to legislators, Elliott turns from a positive (or descriptive, albeit very brief) introduction, to the normative (or prescriptive) issues of how the programs should be structured, the budget rules that should be adopted, the human resources that should be harnessed to manage the programs efficiently, and the tools those managers should use. Considering how much of value he has to say and the important questions he raises on how the programs should be run, it is reasonable for him to restrict his paper to normative issues. However, I suggest that the questions he raises cannot be answered successfully without first understanding
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.111 | 0.075 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".