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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.159 | 0.411 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".