Bibliographic record
Abstract
The juncture of “law and technology” from a legal education point of view is an interesting one. Successfully engaging with law and technology requires stu- dents (of all ages and stripes) to absorb at least some of the substance of many discrete areas of law, as well as to assess how technology creates nexuses between them and challenges some of their underlying notions. As electronic commerce increasingly becomes the bread and butter of many law practices, this need comes into sharper relief — one has to grasp a large variety of fundamentals and simultaneously generate some insight as to where technology is pushing them. Diving into this pool as a student can be daunting. In the latest edition of Legal Issues in Electronic Commerce, Professor R.L. Campbell of Carleton University’s Law and Le- gal Studies Department has continued a successful effort at easing this transition.\nIt is important at the outset to accurately describe this text, which is part of the “Canadian Legal Studies Series” of books published by Captus Press. That series, as described in the publishing blurb on the back of this book, provides texts that contain “articles, cases and analyses that are suitable for legal studies, law and society programs, or related courses in other disciplines.” Accordingly, this book is not a resource for e-commerce practitioners, nor is it designed for teaching an e-com- merce or law and technology course in a law faculty. Rather, it is geared towards undergraduate legal studies programs and, as might be expected, it operates at a slightly more basic level.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.061 |
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".