Bibliographic record
Abstract
The article joins the current debate between epistemic and procedural democrats in contemporary democratic theory and aims to put epistemic democracy on a more secure footing. Yet, unlike those who explore the question from the bottom-up by analyzing the relationship between “truth” and the “fact of disagreement” within the context of domestic political discourse, I adopt a top-down approach animated by political realism and situate democracy within the actual world that we live in: a competitive ecology of states and regimes. The article thus has two purposes. For those who are interested in the recent revival of realism in political theory, it shows how it can be combined with both the epistemic paradigm in democratic theory and the realist research program in international relations, including the neo-positivist strand that has dominated the field over the past four decades. And for those who see themselves as epistemic democrats, it provides a powerful realist argument to defend their conception of democratic authority against criticisms made by procedural democrats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".