Bibliographic record
Abstract
Cette chronique presente cinq decisions d’interets en droit du numerique rendues par les tribunaux canadiens en 2020. La chronique s’ouvre sur la decision de la Cour supreme du Canada dans l’affaire Uber Technologies Inc c. Heller qui a opere un recalibrage inattendu du droit des contrats au regard des nouvelles realites socio-economiques du contexte numerique. Dans un second temps, la chronique aborde l’affaire CompuFinder (3510395 Canada Inc) c. Canada dans laquelle la Cour federale a notamment confirme la constitutionnalite de la Loi canadienne anti-pourriel. La chronique se poursuit avec la decision de la Cour federale dans l’affaire Choueifaty c. Canada qui est revenue rappeler au Commissaire aux brevets le test devant s’appliquer en matiere de brevetabilite de logiciel. Ensuite, la chronique discute de l’enregistrement par le Tribunal de concurrence de l’entente entre le Commissaire a la concurrence et Facebook Inc qui marque une etape majeure pour le droit de la concurrence a l’ere numerique et la protection des canadiennes et canadiens en ligne. Enfi n, la chronique se conclut avec la decision de la Cour superieure de l’Ontario dans l’affaire Sole Cleaning c. Chu au sujet de denonciation sur les reseaux sociaux d’actes racistes dans le milieu de travail. Pour chacune de ces decisions, la chronique en presente les faits, les conclusions, pour ensuite en discuter la portee eu egard aux enjeux du contexte numerique.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".