Practical Applications of Alpha, Beta, and Now… Gamma
Bibliographic record
Abstract
The authors of this <b><i>Journal of Retirement</i></b> article present a new metric they call gamma. It’s designed to quantify how more intelligent financial planning decisions can add value in the form of increased wealth accumulation and retirement income. The concept of gamma has obvious practical applications for financial planners. Less obvious, perhaps, but equally significant, is the positive impact it can have on investment management firms. Read this <b><i>Practical Applications</i></b> report to find out how to quantify financial advice in terms of additional generated income, and to see how the results compare with those of portfolios that lacked such advice. “The difference is fairly stark,” states co-author Paul Kaplan, Director of Research at <b>Morningstar Canada</b>, in an exclusive interview. “In reality, a lot of advisors do not provide financial planning advice,” contends co-author <b>David Blanchett</b>, Head of Retirement Research at <b>Morningstar Investment Management</b>.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".