Bibliographic record
Abstract
Abstract Despite its emergence in the social policy and civil justice arenas more than a quarter of a century ago, there is no federally established definition for elder abuse and no single entity that collects and analyzes elder abuse data. Advances in health and wellness have resulted in an aging population that has more than tripled since 1900. The year 2011 marks the first year that the “baby boomers” will turn 65 years old. The “oldest old,” age 85 and older, is currently the fastest‐growing segment of the population. While it is impossible to accurately assess the number of elder abuse cases, prevalence studies indicate that more than 2 million elderly persons are victims of abuse or neglect each year. The abuse can be physical, sexual, emotional, or financial; it can occur in a variety of environments; and it can be at the hands of a loved one or a stranger. Even a single episode of neglect, mistreatment or abuse can have devastating and fatal consequences for a frail individual. Much further examination and continued investigation into the risk factors and markers of elder abuse are imperative to successfully combat and reduce harmful behaviors targeted toward the aging population.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".