INTEGRATIVE ANALYSIS OF LONGITUDINAL STUDIES ON AGING AND DEMENTIA (IALSA)
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
Cross-validation of research findings across independent longitudinal studies is essential for building the most effective evidence base for successful cumulative science in gerontology. In many cases, cross-study differences in measurements and sample composition (e.g., ability level, education, language) impede the utility of pooled data analysis, particularly in the case of longitudinal studies. Harmonization can occur at the levels of research question, statistical models, and measurements, permitting synthesis of results for understanding ways in which birth cohort, country, culture, and issues of mortality and selection relate to outcomes and differences across studies. The goal of the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA: NIH/NIA P01AG043362) research network encompassing over 100 studies from around the world is to maximize opportunities for international reproducible research and cross-validation across heterogeneous sources of evidence by evaluating comparable statistical models, with comparison of the pattern and magnitudes of effects at the construct level. This symposia describes network activities and methods and provides multiple examples for rigorous cross-study comparison based on the coordinated analysis approach.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it