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Record W2910905312 · doi:10.22215/etd/2014-10207

The Lawfulness-Lawlessness Continuum: Interpersonal Dynamics in Conflict Resolution Processes

2014· dissertation· en· W2910905312 on OpenAlexaff
Rosemary Parker

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsCarleton University
Fundersnot available
KeywordsLawlessnessAdjudicationConflict resolutionPolitical scienceMediationDispute resolutionNegotiationLawLaw and economicsSociologyPolitics

Abstract

fetched live from OpenAlex

The ways in which citizens behave, interact, and plan for the future have cumulative significance for the nature and future of society. This project proposes an analytical device (the lawfulness and lawlessness continuum) to describe the types of behaviours and decision-making that individuals use when participating in adjudication, mediation, and other conflict resolution processes. Lawfulness is a dynamic that is cyclical, orderly, and restrained. I suggest that it can be used to analyze the types of interactions between citizens that are associated with law and adjudication. Lawlessness is a dynamic that is emergent, adaptive, and responsive; it may manifest more often in moments between individuals who are using mediation to resolve their conflicts. Interactions between citizens are likely to involve a combination of lawfulness and lawlessness, along the continuum that lies between them. This analytical model offers a lens through which these nuances of citizen interactions can be analyzed. I would like to thank my supervisor, Neil Sargent, for his enthusiasm and support for this project; his unique ability to help an idea to grow, deepen, and flourish has been invaluable to me. Thank you also to Sheryl Hamilton, my second reader, whose openmindedness, honesty, and perspective helped to bring the project to the next level. The faculty and staff of the Law and Legal Studies Department at Carleton have been very supportive throughout this process

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 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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.932
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.359
Teacher spread0.327 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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