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Simulated Clients Challenge Conventional Legal Education Practices

2019· article· en· W7005919008 sur OpenAlexaboutno aff

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineMaterials Science
ThématiqueDiatoms and Algae Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMemorizationPresentation (obstetrics)InterviewLegal educationLegislationLegal writingProcess (computing)Variety (cybernetics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

TORONTO, Wednesday, February 6, 2019 – In what is one of the latest innovations in legal education, York University’s Osgoode Hall Law School is training people from a variety of backgrounds to be simulated clients and help law students develop their client-facing skills.\n“Outside of law school clinics, it’s as close to the reality of working with clients as most of our students will get,” said Paul Maharg, a leading scholar in legal education who joined Osgoode in 2017 as Distinguished Professor of Practice.\n“Like student doctors meeting real patients in surgeries and hospitals, student lawyers learn to shift their thinking from the technical details of appellate cases and legislation they learn in most courses in law school to a holistic appreciation of a client’s situation, wishes, expectations and the possible extra-legal solutions that might be available to the client.”\nMaharg and Professor Shelley Kierstead are using 11 simulated clients in a pilot program this winter involving Juris Doctor (JD) students in Kierstead’s first-year Legal Process course.\n“We’re using the simulated clients with students to help develop students’ interviewing skills, their awareness of clients, the role of affect, perspective and perception of law in clients, and much else,” Kierstead said.\nThe simulated clients participate in an intense four-day training course before meeting the students. The simulated clients must be able to memorize a scenario and represent it conversationally; improvise on the scenario where appropriate; assess students’ client-facing skills; and self-monitor their own performances as simulated clients.\nMeanwhile, Maharg and Kierstead prepare the students to meet the simulated clients with a brief presentation on the simulated client initiative and a tutorial on interviewing skills. This is followed by a student’s mandatory meeting with a simulated client and a second, voluntary meeting with a simulated client.\nUntil now, students or actors have been mostly used to play the roles of clients. There have been problems with that approach including concerns about the authenticity and fairness of the client experience.\n“The simulated clients are, paradoxically, more authentic because they are trained to enact being themselves with each student,” he said. “We also train them to react conversationally with the lawyer, not to give the full problem as a highly detailed, linear, logical narrative but to present as if the client were relating to the lawyer for the first time, with narrative gaps, redundancies and other markers of conversational register.”\nWhen actors are used with students, they are almost never used to assess students, Maharg said. “In our initiative, we use the simulated clients to assess students’ client-facing behaviours and attitudes. We make client experience the focus of the assessment and ensure the validity and robustness of the assessment.”\nSince 2005 about a dozen simulated client projects have been established internationally among a loose consortium of law schools, legal educators and legal education regulators, Maharg said.\n“In addition to the benefits to this approach, it also challenges many aspects of conventional legal educational practices and cultures,” Maharg said. “In future years, we hope to expand the use of simulated clients in the Law School.”\n-30-\nAbout Osgoode Hall Law School\nOsgoode Hall Law School of York University has a proud history of 130 years of leadership and innovation in legal education and legal scholarship. A total of about 900 students are enrolled in Osgoode’s three-year Juris Doctor (JD) Program as well as joint and combined programs. The school’s Graduate Program in Law is also the largest in the country and one of the most highly regarded in North America. In addition, Osgoode Professional Development, which operates out of Osgoode’s facility in downtown Toronto, offers both degree and non-degree programming for Canadian and international lawyers, non-law professionals, firms and organizations. Osgoode has an internationally renowned faculty of 60 full-time professors, and more than 100 adjunct professors. Our respected community of more than 18,000 alumni are leaders in the legal profession and in many other fields in Canada and across the globe. \nAbout York University\nYork University is known for championing new ways of thinking that drive teaching and research excellence. Our students receive the education they need to create big ideas that make an impact on the world. Meaningful and sometimes unexpected careers result from cross-discipline programming, innovative course design and diverse experiential learning opportunities. York students and graduates push limits, achieve goals and find solutions to the world’s most pressing social challenges, empowered by a strong community that opens minds. York U is an internationally recognized research university – our 11 faculties and 26 research centres have partnerships with 200+ leading universities worldwide. Located in Toronto, York is the third largest university in Canada, with a strong community of 53,000 students, 7,000 faculty and administrative staff, and more than 300,000 alumni. York U’s fully bilingual Glendon campus is home to Southern Ontario’s Centre of Excellence for French Language and Bilingual Postsecondary Education.\nMedia Contacts:\nVirginia Corner, Communications Manager, Osgoode Hall Law School of York University, 416-736-5820, vcorner@osgoode.yorku.ca\nGloria Suhasini, York University Media Relations, 416-736-2100 ext. 22094, suhasini@yorku.ca

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,141
Score d'incertitude au seuil0,281

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,022
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0140,013
Communication savante0,0110,007
Science ouverte0,0050,015
Intégrité de la recherche0,0050,008
Charge utile insuffisante (le modèle a refusé de juger)0,0510,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,363
Écart entre enseignants0,330 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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