Book Review: 'E-Discovery in Canada' by Todd J. Burke, Kelly Friedman, Andrew J. Mccreary, James Morton, Susan Nickle, Vincenzo Rondinelli, Glenn Smith, James Swanson & Susan Wortzman
Bibliographic record
Abstract
It is not hyperbolic to say that the proliferation of electronically stored information (ESI) is probably the most prominent change-harbinger and potential havoc-wreaker in civil litigation today — second only, perhaps, to the spiralling costs of litigation itself. Indeed, the practical and legal difficulties associated with the storage, gathering, preservation, disclosure and evidentiary use of ESI have the potential to act as a Trojan Horse, causing what would previously have been ordinary cases to implode under their weight. Increasing recognition of this is evident; electronic discovery (e-discovery) cases have begun to emerge in the reports, a successful co-operative effort by Bench and Bar to develop governing principles has emerged, and one province has even generated a new stand-alone civil procedure rule on “electronic disclosure.” Some resources on ESI have been available before, but the new book E-Discovery in Canada is the most comprehensive yet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".