Bibliographic record
Abstract
Abstract: In a pioneering article entitled “Taxation by Regulation,” Judge Richard Posner challenged the prevailing orthodoxy that regulation emulates competition along the lines of the Public Interest Theory of regulation. He argued that regulation is best viewed as a branch of public finance in which the power of the state is leveraged to achieve non-competitive outcomes. We develop an indirect test of Posner’s theory by specifying the regulator’s welfare function as a convex combination of consumer surplus, profits shared with the regulator and profits retained by the regulated firm. The welfare weights cannot be observed directly, but can be inferred from the regulator’s behavior in equilibrium. To wit, when the regulator permits the regulated firm to earn positive profits and authorizes higher prices in response to a greater degree of profit sharing this establishes both an upper bound on the consumer surplus weight and a higher weight on shared profits than on profits retained by the regulated firm. Applying this test to the implementation of the 1996 Telecommunications Act lends support to Posner’s theory.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".