Analytic eclecticism and International Relations: Promises and pitfalls
Bibliographic record
Abstract
Some scholars in International Relations and comparative politics continue to debate how to obtain the strongest explanatory theory whereas others hold that each approach should be treated as its own area of research. Both of these groups tend to agree that factors from across paradigms cannot be coherently combined with each other. On the contrary, Sil and Katzenstein have argued for analytic eclecticism in scholarship, which would not treat research traditions or paradigms as strict limitations on theory construction. Inspired by pragmatism, they have made a compelling case that considerations of usefulness and knowledge cumulation are more important than paradigmatic fidelity. This forum examines analytic eclecticism from the points of view of neo-empiricism, feminism, and interpretive constructivism, followed by a reply by Sil. A decade has passed since the publication of Sil and Katzenstein’s Beyond Paradigms, so it seems appropriate to reflect upon the strengths and weaknesses of analytic eclecticism.
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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 | 0.117 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.137 |
| Scholarly communication | 0.021 | 0.059 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.012 | 0.027 |
| 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; 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".