Multiple Dialogues, Layered Syntheses, and the Limits of Expansive Cumulation
Bibliographic record
Abstract
Our contribution to this forum will focus on four observations about dialogue and synthesis in international relations. (1) Meaningful discussion of these and related issues will remain elusive if we continue to focus exclusively on the broadest level of analysis. There are multiple layers to orientations in the study of world politics that when combined produce irresolvable debates within epistemic and theoretical communities, making synthesis virtually impossible to realize, notwithstanding the examples to the contrary offered in this forum by Andrew Moravcsik. (2) Even though pluralism has its virtues, an uncritical application of this principle leads to logical contradictions that are generally ignored by postpositivists and other proponents of diversity and expansive cumulation. (3) If we follow Yosef Lapid's recommendation to focus on the middle ground running through these debates, we would generate more, not less, division. (4) We argue that focusing more explicitly on the logical link between theory and empirical evidence is the only available interpretative tool that can help mediate many of these debates, cut through the morass of alternative theoretical interpretations, and provide insights into and guidance for selective cumulation.
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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 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.023 | 0.038 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.007 |
| 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".