Bibliographic record
Abstract
This very interesting study by Kevin Davis and Michael Trebilcock proposes a quantification of the economic benefits of bijuralism. The task is not an easy one as it poses major conceptual challenges, including the very definition of the value of bijural training. The purpose of this commentary is to support an economic analysis of bijural training that goes somewhat beyond the concept of bijural training as the 'acquisition of knowledge from two different systems', on which the Davis and Trebilcock study is based. Firstly, the author will present a concept of bijural training that is focused on what the author calls legal dexterity rather than on knowledge acquisition and the implications of this concept for an economic theory on bijuralism. Secondly, the author will briefly comment on the congruence between the demand for bilingual an for bijural lawyers that emerges from the Davis and Trebilcock study. Finally, the author will suggest that an economic analysis of bijuralism must also be concerned with the comparative cost of such training. The economic benefits of bijural training, as difficult as they are to measure, can be acquired in Canada at a relatively low cost. From this perspective, therefore, bijuralism should be seen as an economic benefit to be cultivated.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".