The Common Good and Legal Interpretation: A Response to Leonid Sirota and Mark Mancini
Bibliographic record
Abstract
Recent debates in the United States and Canada bear witness to a renewed interest in the moral foundations of legal interpretation. This article offers a contribution to the ongoing debate in Canadian legal circles, responding to critiques by Leonid Sirota and Mark Mancini of the emergent theories of “common good originalism” and “common good constitutionalism”. Contrary to our interlocutors’ view, the natural law tradition does not “look to extraneous moral and policy commitments as guides for legal interpretation” nor does it see the law as a mere instrument to achieve “pre-determined outcomes.” Rather, it regards the positive law and natural law as reciprocally interrelated; natural law lends intelligibility to the positive law, while positive law makes concrete the abstract precepts of natural law. This perspective provides valuable insights into the activity of legal interpretation, as directed toward understanding and giving effect to the legislature’s reasoned choices for the common good.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".