Taking Ideas Seriously in Political Science: The Diffusion of Presidentialism in Latin America after Independence
Bibliographic record
Abstract
Abstract Today it is rare to find a political scientist who rejects that “ideas matter”—at least in the abstract. But if ideationally inclined theorists have gained seats at the disciplinary table, their approaches still occupy disadvantaged positions. Ideational theories typically must confront nonideational alternatives to achieve salient publication, but nonideational theorists routinely design and publish research without considering ideational alternatives. This is even true on topics where all scholars seem to agree a priori on the importance of ideas. Erratic attention to ideas appears to be justified by widespread views that even if ideas plausibly “matter,” they are too intractable to address in concrete research: too difficult to measure empirically, to relate in explanatory ways to action, or to connect to goals of theoretical generalization. This article first highlights major problems with these views in the abstract, and then illustrates them in the example of early Latin American constitutional design. On this terrain, there are good reasons to think that an ideational account connects in more concrete ways to available evidence than leading nonideational hypotheses about constitutional choice. Political science should move toward better balanced debates between plausible explanations, upgrading the rigor of the discipline overall.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".