Model-based Clustering and Typologies in the Social Sciences
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
Social scientists spend considerable energy constructing typologies and discussing their roles in measurement. Less discussed is the role of typologies in evaluating and revising theoretical arguments. We argue that unsupervised machine learning tools can be profitably applied to the development and testing of theory-based typologies. We review recent advances in mixture models as applied to cluster analysis and argue that these tools are particularly important in the social sciences where it is common to claim that high-dimensional objects group together in meaningful clusters. Model-based clustering (MBC) grounds analysis in probability theory, permitting the evaluation of uncertainty and application of information-based model selection tools. We show that the MBC approach forces analysts to consider dimensionality problems that more traditional clustering tools obscure. We apply MBC to the “varieties of capitalism,” a typology receiving significant attention in political science and economic sociology. We find weak and conflicting evidence for the theory's expected grouping. We therefore caution against the current practice of including typology-derived dummy variables in regression and case-comparison research designs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| 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