Preregistration of secondary data analysis: A template and tutorial
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
Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained popularity in recent years. However, the current guidelines for preregistration have been developed primarily for studies where new data will be collected. Yet, preregistering secondary data analyses---where new analyses are proposed for existing data---is just as important, given that researchers’ hypotheses and analyses may be biased by their prior knowledge of the data. The need for proper guidance in this area is especially desirable now that data is increasingly shared publicly. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses and provide comments and a worked example that may help with using the template effectively. Through this illustration, we show that completing such a template is feasible, helps limit researcher degrees of freedom, and may make researchers more deliberate in their data selection and analysis efforts.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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