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
Group investment decisions confront the challenge of meeting diverse needs. Existing practice seems to be mostly ad hoc. Even where model based, it may be too complex or idealistic to be implemented. This article proposes an extension to the expected utility framework for simplified group asset allocation. The authors’ approach combines utility functions using positive linear weights, accommodating variations in risk aversion, tax treatment, allocation frequency, funding risks, and nominal versus inflation-adjusted returns. By incorporating personal portfolios within a portfolio network to be optimized simultaneously with a pooled portfolio, potential conflicts among group members are further reduced. The procedure employs multiple matrixes to represent return probabilities as they affect the ability to safeguard goals as viewed by each individual. Behavioral finance’s goal-based investing is simplified using Rubinstein utility to seek growth while reducing the probability of failure to meet group member goals. For those able to produce financial plans with positive surplus, Rubinstein utility also supports more objectively appropriate risk aversions. The outcome is a practical and rigorous method for strategic group asset allocation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it