Increasing Innovation in Legal Process: The Contribution of Collaborative Law
Bibliographic record
Abstract
This dissertation examines the role of innovation in resolving complex disputes, using Collaborative Law as its case study. Innovation, for the purposes of this research, can be defined as applied creativity that leads to optimal resolution for clients. The process of innovation is required to resolve complex problems, which are increasingly prevalent in legal, economic and social spheres. Collaborative Law indeed has the capacity to resolve such issues in the legal realm. Collaborative Law is a process by which parties and their lawyers enter into a binding contract that limits the representation to a facilitative problem-solving process with the intent to reach a negotiated settlement. Through an interdisciplinary team approach that employs a sequenced negotiation process, complex problems can be aptly and innovatively resolved through Collaborative Law.\nThis research examines the capacity of Collaborative Law to resolve complex problems using methods of ethnographic study, specifically participant observation and key informant interviews. Attendance at conferences and practice group meetings provided the researcher with insight through observation. The researcher subsequently interviewed 31 lawyers who practise Collaborative Law in four Canadian research sites, namely, Halifax, Simcoe County, Toronto and Vancouver. Through these interviews and observations, common themes were generated. When superimposed atop of innovation theory, this research demonstrates that Collaborative Law supports innovation on both a macro and micro level.\nCollaborative Law itself is an example of an innovative process and individual innovations are possible in executing the Collaborative Law process, where used and executed appropriately. These results have implications for Collaborative Law practice, for the practice of law, and for legal education that will be explored through this study. Such implications will be examined, along with suggestions for future research.
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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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".