From Data to Discovery: Unsupervised Machine Learning's Role in Social Cognition
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
The study of how cognition and society interact is a complex endeavor that demands multiple methods and tools. Yet research in social cognition has only begun to capitalize on unsupervised machine learning (UML) tools that can uncover hidden patterns in data. In this tutorial, we introduce UML as a complementary approach to traditional statistical methods. We illustrate four methods (K-means clustering, Density-Based Clustering of Applications With Noise [DBSCAN], Principal Component Analysis [PCA], and Market Basket Analysis) applied to data from Project Implicit and the Implicit Association. We show how UML can identify patterns and relationships that conventional methods might overlook. Throughout, we provide clear (and openly available) code and highlight important researcher decision points in implementing UML in social cognition work. By bringing the advances of UML into social cognition, we will be better equipped to tackle larger, more diverse, or multilevel data sets that reveal the complexities of our social world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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