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
OBJECTIVES: The objectives of this Introduction to the Journal of Gerontology: Psychological Sciences special issue on "50 Years of Cognitive Aging Theory" are to provide a brief overview of cognitive aging research prior to 1965 and to highlight significant developments in cognitive aging theory over the last 50 years. METHOD: Historical and recent theories of cognitive aging were reviewed, with a particular focus on those not directly covered by the articles included in this special issue. RESULTS: Prior to 1965, cognitive aging research was predominantly descriptive, identifying what aspects of intellectual functioning are affected in older compared with younger adults. Since the mid-1960s, there has been an increasing interest in how and why specific components of cognitive domains are differentially affected in aging and a growing focus on cognitive aging neuroscience. DISCUSSION: Significant advances have taken place in our theoretical understanding of how and why certain components of cognitive functioning are or are not affected by aging. We also know much more now than we did 50 years ago about the underlying neural mechanisms of these changes. The next 50 years undoubtedly will bring new theories, as well as new tools (e.g., neuroimaging advances, neuromodulation, and technology), that will further our understanding of cognitive aging.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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