Data Dispersion in Economics(II) - Inevitability and Consequences of Restrictions
Bibliographic record
Abstract
This article reviews and improves the theorems of the existence of restrictions near the boundaries of finite numerical segments and of the probability scale in the presence of non-zero dispersion. The non-zero dispersion may be caused, for example, by the influence of observation noises. Applications of the theorems to experiments, which are typical of the utility theory, are briefly presented. Similar experiments may be associated with the old problems of utility theory, such as the underweighting of high and the overweighting of low probabilities, risk aversion, loss aversion, the Allais paradox, the equity premium puzzle, the "four-fold pattern" paradox, etc. It is shown that the restrictions as the consequences of the theorems should be taken into account in the explanation of such experiments. The restrictions may facilitate such explanations including explanations by utility models.
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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 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".