Documenting Variable Comparability with DDI-Lifecycle
Bibliographic record
Abstract
ICPSR has recently been actively engaged in moving to DDI-Lifecycle to document some of its longitudinal data. Pilot projects involving the creation of DDI-L metadata for two of our most popular longitudinal studies have already been finalized, and the variable-level documentation for the National Social Life, Health, and Aging Project (NSHAP) is now publicly available for online searching and exploring comparability across waves. Using this previous work as a background, we will focus our presentation on a new, ongoing project that uses DDI-Lifecycle to document comparability between two independent longitudinal collections – the NSHAP and the National Health and Aging Study (NHATS) - that explore similar topics, with special focus on health and cognition issues among aging populations. We will elaborate on the steps, the tools we used, and the decisions taken to move this project forward, and will share practical details regarding its organization and progress. We will also include our findings regarding potential difficulties and benefits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.083 | 0.014 |
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; both teacher heads agree on what is shown here.
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".