Coordinated data analysis: A new method for the study of personality and health
Bibliographic record
Abstract
A majority of research by personality psychologists examining health has utilized publicly available datasets, for good reason. These resources are often the only available datasets large enough to detect expected effect sizes and may contain biological or genetic data that is difficult to obtain. However, researchers tend to examine only one large dataset at a time. Given recent meta-research on the robustness and replicability of "established" findings, all researchers should take greater care to evaluate the evidentiary value of their findings and seek methods to increase their robustness. Personality and aging psychologists who use publicly available datasets have a unique tool at their disposal in order to achieve this goal, namely, more publicly available datasets. More specifically, psychologists may use coordinated analysis (Hofer and Piccinin, 2009; Piccinin and Hofer, 2008) to examine relationships across several large datasets and, using the tools of meta-analysis, identify generalizable effect sizes and examine heterogeneity across countries and methods. This chapter describes the motivation for coordinated analysis, the process of using this method, and details several examples.
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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 | 0.007 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 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".