Bibliographic record
Abstract
During the year 2002, I was a firsthand witness to a historically unprecedented pension experiment that took place in the state of Florida. Every one of that state's more than 500,000 public employees—in addition to every new employee joining the state's payroll—was given the option of converting their traditional defined benefit (DB) pension plan into an individually managed defined contribution (DC) account. The DC investment plan was similar to a corporate-style 401(k) plan, under which the employee has full control over asset allocation and investment decisions. Florida's new Public Employee Optional Retirement Program (PEORP) was the focus of intense scrutiny by local and national media. This is because it was the largest such pension conversion in the history of the United States and was viewed by many observers as a potential laboratory for Social Security reform. Although at first the take-up rate for the DC plan was low, it is now estimated that over half of the state's new employees have decided to forgo the traditional DB pension and instead enroll in the DC investment plan. This large-scale transition from DB pension to DC accounts is not limited to the state of Florida or the United States alone. A number of other states— including a failed attempt by California Governor Schwarzenegger—have proposed converting their public employee DB plan into either a mandatory or optional DC plan. Several countries around the world—starting most prominently with Chile in the mid-1980s—have introduced DC-style pension savings accounts as an alternative to traditional DB pensions.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.528 | 0.431 |
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".