The relation of implicit age bias based on negative age stereotypes to the American state prevalence of older adult Alzheimer’s disease
Bibliographic record
Abstract
This study determined the relation of Implicit Age Bias among respondents aged 20–59 years of age to the 2020 Alzheimer’s disease (AD) prevalence among residents 65 years and over with the 48 contiguous American states as analytic units. This implicit measure of state ambient ageism correlated .69 with state AD prevalence and persisted in multiple regression equations considering several controls including older adult poverty rate, high school graduation, bachelor’s degree attainment, and multiple chronic conditions. Based on stereotype embodiment theory, the assumption is that the influence of external state-level age bias combined with the personal experiences of state residents leads to the general internalization of negative age stereotypes and ultimately to higher state AD prevalence. The speculation is that such internalization at the individual level leads to adoption of unhealthy behaviors and stress accumulation that eventually produces immunological deficiencies, infections, and inflammation conducive to AD onset and progression.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".