Modernizing Political Science: A Model-Based Approach
Bibliographic record
Abstract
Although the use of models has come to dominate much of the scientific study of politics, the discipline's understanding of the role or function that models play in the scientific enterprise has not kept pace. We argue that models should be assessed for their usefulness for a particular purpose, not solely for the accuracy of their predictions. We provide a typology of the uses to which models may be put, and show how these uses are obscured by the field's emphasis on model testing. Our approach highlights the centrality of models in scientific reasoning, avoids the logical inconsistencies of current practice, and offers political scientists a new way of thinking about the relationship between the natural world and the models with which we are so familiar.Kevin A. Clarke is Assistant Professor, Department of Political Science, University of Rochester (kevin.clarke@rochester.edu) and David M. Primo is Assistant Professor, Department of Political Science, University of Rochester (david.primo@ rochester.edu). Earlier versions of this paper were presented at the 2004 Annual Meeting of the American Political Science Association and at the 2005 Annual Meetings of the Midwest Political Science Association and the Canadian Political Science Association; we thank the participants for their comments. We thank Chris Achen, Jim Alt, Jake Bowers, Henry Brady, Bear Braumoeller, John Duggan, Mark Fey, Rob Franzese, John Freeman, Gary Goertz, Miriam Golden, Jim Granato, Gretchen Helmke, John Jackson, Keith Krehbiel, Skip Lupia, Scott de Marchi, Andrew Martin, Becky Morton, Bob Pahre, Kevin Quinn, Curt Signorino, Randy Stone, and three anonymous reviewers for helpful comments and discussion. We also thank Matt Jacobsmeier for research assistance. Support from the National Science Foundation (Clarke: Grant #SES-0213771, Primo: Grant #SES-0314786) is gratefully acknowledged.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".