Bibliographic record
Abstract
In an effort to advance toward achieving this goal, the Cyberjustice Laboratory, supported by a multidisciplinary group of 36 international researchers and funded by the Social Sciences and Humanities Research Council, launched a 7-year research project in 2011: Towards Cyberjustice. 1 The project's main hypothesis was that information and communication technologies could significantly contribute to improving traditional legal processes as well as entirely modifying the conventional structure of trials.In this light, the research conducted was aimed at identifying and developing concrete solutions that could optimize traditional legal processes and ultimately enhance the administration of justice as a whole, such that efficiency would be increased, costs and delays would be reduced, and mechanisms would be simplified.While many attempts have been made toward achieving this goal throughout the legal world, as will be discussed in more detail below, the project's novelty and success lies in two unique factors.To begin with, it conducts socio-legal studies regarding both the impacts of technology on law and the identification of rituals and practices that hinder the networking of the justice system.Additionally, through techno-legal studies funded mainly by the Canadian Foundation for Innovation, it simultaneously develops opensource software solutions that are adapted to judicial and extrajudicial contexts and can be tailored to the varying needs of each individual justice system.This cross-fertilization of socio-legal and techno-legal studies not only allows for the development of technological tools tailored to the justice system, but also makes it possible to substantially re-examine the judicial process in a manner that is primarily designed to improve access to justice.These various studies that emerged from the Towards Cyberjustice project were conducted by an elaborate team of international researchers from twenty universities worldwide, separated into three working groups, each of which was dedicated to examining a differing and particular aspect of the research in question.The first working group, whose research will be discussed in further detail in the first part of this collection, considered (a) the digitalization of justice and its interaction with the values inherent in the justice system.The second working group, whose aim was
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.531 | 0.322 |
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".