Are Jurisprudential Debates Conceptual?: Some Lessons from Democratic Theory
Bibliographic record
Abstract
The dominant view among legal philosophers is that jurisprudential debates about the nature of law are conceptual. In this article I challenge this view. I do so by comparing these debates to debates about the justification of democracy and showing that the arguments found in both are often very similar. I demonstrate that in both domains, there are arguments on one side that explain an institution (either law or democracy) in terms of its ability to help people lead a better life, and there are arguments on the other side that highlight the value of these institutions in promoting political participation. The arguments for democracy are unquestionably normative, and I argue this suggests that the jurisprudential arguments are normative as well. Based on this conclusion, I then offer some tentative suggestions for rethinking familiar jurisprudential terrains—now usually understood as dealing with conceptual questions—in normative terms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".