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
Abstract In countries with large scale private and public funded pension arrangements, for example, the United States, Canada, Japan, the Netherlands, and the United Kingdom, one of the key decisions is how the contributions into the fund should be invested to best effect. The investment decision typically results in some form of risk sharing between members and sponsor in terms of (1) the level of contributions required to pay for all promised benefits, (2) the volatility of contributions required, and (3) the uncertainty of the level of benefits actually deliverable should the scheme be wound up or have to be wound up. Some of the risks associated with the pension benefits have a clear link with the economy and hence with other instruments traded in the financial markets. Others, such as demographic risks and the uncertainty as to how members or the sponsor will exercise their options, which are often far from being economically optimal, are less related to the assets held in the fund. This article describes broadly what assets are available to the institutional investor, how an investor might go about deciding on an asset allocation, and explore what the possible consequences of an asset allocation might be.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it