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Record W4312119814

LA CYBERJUSTICE COMME RÉPONSE AUX BESOINS JURIDIQUES DES PERSONNES ITINÉRANTES: SON POTENTIEL ET SES EMBÛCHES

2013· article· fr· W4312119814 on OpenAlexaff
Suzanne Bouclin, Marie-Andrée Denis-Boileau

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languagefr
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Le présent texte cherche à déterminer si le développement de la cyberjustice améliorera ou compromettra l’accès à la justice pour les personnes en situation d’itinérance. Nous commencerons par une revue détaillée de la littérature actuelle sur la cyberjustice, pour ensuite présenter notre définition de la cyberjustice et de son rôle dans la promotion de l’accès à la justice. Dans une dernière section davantage exploratoire, nous élaborerons quelques propositions informelles de réponse à la question formulée précédemment – à savoir si la cyberjustice, dans ses formes actuelles, peut contribuer à répondre aux besoins juridiques des personnes en situation d’itinérance. Ces réponses seront évaluées dans une recherche ultérieure.\n\n \n\nThis aim of this paper is to determine whether the expansion of cyberjustice will improve or impede access to justice for individuals who are homeless. We begin with a detailed review of the current literature on cyberjustice, followed by our definition of cyberjustice and the role it plays in promoting access to justice. The last section, which is more exploratory in nature, offers a number of informal proposals for answering the question of whether cyberjustice, in its present forms, can help to meet the legal needs of homeless persons. Further research will be done in order to evaluate those answers.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0100.012
Open science0.0050.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.269
GPT teacher head0.498
Teacher spread0.229 · 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.

Study designObservational
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
Published2013
Admission routes1
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicHate Speech and Cyberbullying DetectionFrench-language works237,207