From Data to Discovery: Unsupervised Machine Learning's Role in Social Cognition
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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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 | 0.023 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".