Statistical foundations for assessing the difference between the\n classical and weighted-Gini betas
Bibliographic record
Abstract
The `beta' is one of the key quantities in the capital asset pricing model\n(CAPM). In statistical language, the beta can be viewed as the slope of the\nregression line fitted to financial returns on the market against the returns\non the asset under consideration. The insurance counterpart of CAPM, called the\nweighted insurance pricing model (WIPM), gives rise to the so-called\nweighted-Gini beta. The aforementioned two betas may or may not coincide,\ndepending on the form of the underlying regression function, and this has\nprofound implications when designing portfolios and allocating risk capital. To\nfacilitate these tasks, in this paper we develop large-sample statistical\ninference results that, in a straightforward fashion, imply confidence\nintervals for, and hypothesis tests about, the equality of the two betas.\n
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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 | 0.041 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".