Bibliographic record
Abstract
In his reply to criticism raised against his reading of Rawls’s constructivist method in light of Rorty’s pragmatism, Kai Nielsen defends his position on the basis that it provides the tools for an external defence of liberalism. An external defence seeks to justify a liberal conception of justice in a way that will be acceptable not only to those who have already accepted the core substantive principles of liberal theory, but to those who reject these core principles. This paper assesses Nielsen’s proposal and shows that Rortian pragmatism is a limited way of achieving an external defence of liberal political and legal institutions. I propose to develop instead the idea of Kantian constructivism to this end. This discussion also brings to light broader questions about the idea of practical philosophy, and thus pays tribute to the works of John Rawls, Richard Rorty, and Kai Nielsen.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.028 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".