Higher Thresholds for Elder Abuse with Age and Rural Residence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Attitudes toward elder abuse differ with age, ethnicity, profession, and training. This article introduces a threshold model in order to reconcile findings on attitudinal differences within a unifying theoretical framework. The model assumes that individuals rate the abusiveness using consistent standards but different thresholds. Predictions from the model include consistency among individuals in their ratings of different behaviours (i.e., high relative consistency), but variation in the levels of rating (i.e., systematic departures from absolute consistency). Samples of 339 seniors and 233 professionals rated 112 items representing a wide range of abuse severity. The findings suggested high relative consistency but systematic deviations from absolute consistency, with higher ratings (i.e., lower thresholds) by professionals than seniors, and by residents of smaller (rural) rather than larger (urban) communities. The implications of the threshold model include prevention through elder-abuse education and reporting practices.
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it