Preliminary Are All Americans Saving ‘Optimally ’ for Retirement?
Bibliographic record
Abstract
findings and conclusions expressed are solely those of the authors and do not represent the views of SSA, any agency of the Federal Government or the RRC. We are grateful to our colleague Surachai Khitatrakun for very helpful advice, Jeff Kling for his help in acquiring the restricted HRS data, to the National Institute of Aging and the Social Security Administration for financial support, and to the professionals at the University of Michigan, Rand and the Federal Reserve Board of Governors who developed the HRS and SCF data that we rely on.There is widespread concern expressed in newspapers and in public policy and academic studies that a substantial fraction of Americans are preparing poorly for retirement. The headlines of newspaper articles – two examples are “Debt-Squeezed Gen X Saves Little ” or “Retirement’s Unraveling Safety Net ” – suggest that individuals or the institutions that people rely on for retirement security are falling short. 1 Journalists likely take cues from the financial services industry and from writing by academics and other opinion leaders. An article in the 2007 McKinsey Quarterly (Court, Farrell, and Forsyth, 2007) states “One finding of our research was a segmentation indicating that only about a quarter of the boomers are financially prepared for their twilight years ” (page 106). Munnell, Webb and Golub-Sass (2007) conclude “The
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".