A RISK SCORING MODEL AND APPLICATION TO MEASURING INTERNET STOCK PERFORMANCE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a risk scoring model to assess the performance of 27 US companies listed online by applying Data Envelopment Analysis (DEA) and comparing with the traditional financial measure Return on Equity (ROE). The DEA evaluation process involves two processes: (1) computation of operating efficiency and effectiveness to measure a company's operating performance, and (2) measurement of the return level per unit of risk to provide guidance for their investors. The risk scoring model is useful for both investors and company managers. For investors, it yields a new stock selecting strategy. For managers, it provides a risk-adjusted performance evaluation process. Empirical results show that for the Internet industry, the effectiveness of a company is more important than operating efficiency. Investors investing in efficient online companies yield higher returns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it