Bibliographic record
Abstract
A little more than a quarter-century ago, Professor Ginsburg looked out at tax landscape of multi-year sales and saw the very model of complexity. Over course of next several years, a widening group of tax professionals, inside and outside of government, came to agree with Ginsburg's assessment and with many of his proposed solutions. The result was Installment Sales Revision Act of 1980, which simplified and broadened application of installment method while lessening role of other methods for reporting an installment sale. Although 1980 Act and implementing regulations issued a year later answered many important questions, they did not answer them all. Moreover, some of answers they gave were provisional and subject to modification on basis of practical experience. Professor Ginsburg and his collaborators build house, so to speak, and left it to future courts, tax administrators and commentators to choose interior decoration. In this spirit, purpose of this paper is to pick some nice drapes for living room windows and a suitable carpet for hall. Parts I and II of article trace development of various theories for taxing seller in a multi-year sale or exchange, from turmoil that Professor Ginsburg found in 1975 through present day. A brief Part III touches on determination of interest on deferred payments. Part IV collects themes from this recent history. Part V concludes with several suggestions for next quarter-century or so.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".