High Stakes and Persistent Challenges – A Rejoinder to Klabbers and Augsberg
Bibliographic record
Abstract
Abstract In this separate rejoinder to Jan Klabbers' and Ino Augsberg's comments to the articles in the symposium on New Legal Realism in International Law (Leiden Journal of International Law, Volume 28:2, 2015), we respond from the point of view of the European New Legal Realism (ENLR) as propounded in our initial contribution to the symposium. Agreeing with Ingo Venzke who wrote in his introduction to the symposium that ‘stakes are high’ in the debate over international law and methodology, we argue that both Klabbers and Augsberg, each in their own way, fail to take sufficiently seriously the ENLR challenge to doctrinal scholarship. We argue that Klabbers underestimates the evergreen and persistent character of this challenge when he portrays the current push for New Legal Realism as merely a whimsy fashion wave. And we argue that Augsberg's essentially Kelsenian defence of doctrinal scholarship is insufficiently robust because it inherits the excess epistemological liberalism of its underlying Neo-Kantianism.
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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 | 0.039 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.030 | 0.043 |
| Insufficient payload (model declined to judge) | 0.003 | 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".