I Beg to Differ: Questions about Law, Language and Dissent
Bibliographic record
Abstract
In this paper, we consider the linking of law and language in the space of the judicial opinion, interested particularly in those insights about law and language that can be gained by focusing attention on the space of judicial dissent. In Section 1, we offer some introductory remarks about language and the operations of force and persuasion in judicial decision-making, turning our attention in Section 2 to the specific practice of judicial dissent. In Section 3, we describe a category of dissenting practices that implicate what could be called a 'noetic' space of judgment, and consider how the resources of language might operate in this space. In Section 4, we examine the deployment of language in majority and dissenting opinions, using Mossop v. Canada (a Canadian same-sex family case) as an example. We suggest that there is much to be learned about dissent and judgment by taking an interdisciplinary approach that draws law and the humanities into closer dialogue.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.104 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".