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Record W7019899296

Increasing Innovation in Legal Process: The Contribution of Collaborative Law

2015· article· en· W7019899296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueeYLS (Yale Law School) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsYork University
Fundersnot available
KeywordsNegotiationProcess (computing)CreativityLegal professionLegal researchCollaborative modelLegal educationAttendance
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.345
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it