Typologies: Forming Concepts and Creating Categorical Variables
Bibliographic record
Abstract
Abstract This article describes the categories and typologies as an optic for looking at concept formation and measurement. It also provides an overview of the multiple contributions of typologies and presents numerous examples from diverse subfields of political science. It gives a framework for working with multidimensional typologies, outlining the building blocks of typologies, and illustrating how the cell types constitute categorical variables. In addition, the role of typologies in concept formation, the source of the concepts and terms in the cells of the typology, and the role of ideal types are explained. Finally, it explores the contribution of typologies to mapping empirical and theoretical change and to structuring comparison in empirical analysis. It suggests norms for the careful use of typologies. Among the guidelines for careful work with typologies, a significant priority to keep clearly in view is their contribution to wider goals of formulating and evaluating explanatory claims.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".