Bibliographic record
Abstract
Cognitive decline will increasingly become a workplace concern because of three intersecting trends. First, the American population is aging. In 2019, 16.5 percent of the population, or fifty-four million people, were age 65 and over, and the number is expected to increase to seventy-eight million by 2025. Dementia is not uncommon among older adults, and by the age of eighty-five, between twenty-five and fifty percent of individuals suffer from this condition. Second, individuals are postponing retirement and prolonging their working lives. For example, about a quarter of physicians are over sixty-five, as are fifteen percent of attorneys. The average age of federal judges is sixty-nine. Third, a variety of technologies, such as PET scans, spinal taps, genetic tests, and even blood tests now enable physicians to detect potential signs of dementia long before symptoms emerge. Employers may well be tempted to pursue these diagnostic tools because cognitive decline can cause a multitude of complex challenges in the workplace, threatening productivity, workplace morale, and public safety.The question of how to handle cognitive decline in the workforce has received very limited attention in the legal literature. This Article strives to treat the subject in a balanced way, considering the interests and difficulties faced by all stakeholders: employers, workers, and the public. It examines a variety of strategies that employers could implement, including mandatory retirement ages, mandatory cognitive testing for older employees or all employees, testing for dementia biomarkers, or an approach of individualized assessment. It assesses these approaches in light of the relevant federal laws that prohibit age, disability, and disparate impact discrimination and suggests necessary statutory revisions. The Article concludes with detailed recommendations to help employers, employees, and professional associations appropriately manage this very sensitive matter.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".