Endogeneity and qualitative political analysis: Debates about method or debates about ontology?
Bibliographic record
Abstract
Qualitative political analysis has made substantial methodological progress in the last 25 years. This article examines the contributions to this progress made by the work of three American social scientists (King, Keohane, and Verba, 2021 [1994], hereafter KKV) and the responses that their work provoked. The article identifies a recurring ambiguity in this methodological literature. In the quantitative tradition to which KKV want to hold qualitative methods endogeneity is a methodological problem that induces a search for methodological workarounds. Yet in qualitative work, endogeneity is often more a basic feature of the social and political world that needs to be modeled directly. While there can be substantial theoretical differences in how these features are modeled, the presumption is that endogeneity is more an ontological claim than a methodological problem. The article identifies how this ambiguity first arises in the work of KKV and then traces out the implications through a discussion of a range of methodological options, from process tracing to instrumental variables.
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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 | 0.356 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.009 | 0.137 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".