Bibliographic record
Abstract
The author applies data envelopment analysis (DEA) and uses the basic, cross-evaluation, and super-efficiency models to evaluate the performance of the fund of hedge funds classification (a basket of hedge funds). The purpose of alternative investment strategies such as funds of hedge funds is to offer absolute returns, so using passive benchmarks to measure their performance may be ineffective. With the ever-increasing number of funds of hedge funds, there is an urgency to provide money managers, pension funds, and high-net-worth individuals with a trustworthy appraisal method in ranking their efficiency. DEA can achieve this, and one important benefit of this measure is that benchmarks are not required, thereby alleviating the problem of using traditional benchmarks to examine non-normal returns. This article aims to investigate funds of hedge funds and to identify the funds that have achieved superior performance or, in other words, have an efficiency score of 100 in a risk/return setting.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".