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Enregistrement W7011433746

Lessons from the Carnegie and Best Practices Reports: A Look at St. John's University School of Law's Street Law Program as a Model for Teaching Professional Skills

2009· article· en· W7011433746 sur OpenAlexaboutno aff

Notice bibliographique

RevueeYLS (Yale Law School) · 2009
Typearticle
Langueen
DomaineMedicine
ThématiqueTravel-related health issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLegal educationCurriculumBest practiceLegal professionPractice of lawProfessional developmentFoundation (evidence)Legal research
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

(Excerpt) The attitude toward professional skills in legal education has improved significantly in recent years. Law schools now recognize that there is a need for greater attention to professional skills instruction. Many law schools are experimenting with teaching methods other than the traditional case style of teaching. In fact, one of the leading academic institutions in the United States, Harvard Law School, has already changed its curriculum to offer more skills-based courses to their students. Other law schools have also followed this trend. In 2007, two very influential institutes published reports that favored this new approach to legal education. The Carnegie Foundation for the Advancement of Teaching published its report, "Educating Lawyers: Preparation for the Profession of Law", and the Clinical Legal Education Association published its study, "Best Practices for Legal Education" (collectively, the "Reports"). The Reports focus, in part, on the academy's role in preparing students for practice. They conclude that law schools must devote more attention and resources to helping students develop the professional skills they will need in practice. The consensus was that the traditional case method of teaching, alone, is insufficient in training students. The Reports recommend for law schools to broaden the ways in which they teach their students to become lawyers by, for example, incorporating "settings and pedagogies different from those used in the teaching of legal analysis." They suggest that law schools can unite formal knowledge and the experience of practice by offering non-traditional curricular offerings, such as clinics, externships, simulations, and other similar opportunities. St. John's University School of Law currently offers a unique externship opportunity that effectively integrates doctrine and practice in the way the Reports advance. The Street Law program allows law students to teach a practical law course to high school students in the local community of Queens, New York. The course, including its name, was inspired by and modeled after the Street Law High School Clinic at Georgetown University Law Center, which was the first law school to offer a program of this nature. Using St. John's Street Law program as an illustration, this article demonstrates how non-traditional course offerings can provide powerful professional development opportunities for students. St. John's Street Law program uniquely incorporates many of the recommendations of the Reports. The students' in-depth approach to the law and contact with the community positively shapes their ability to become responsible and skilled legal professionals. Thus, the program serves as an excellent model for how law schools can integrate the teaching of knowledge, skills, and values into their curricula. First, this article describes the history of Street Law in the United States and the current offering at St. John's. Next, it discusses the relevant parts of the Reports and their recommendations for rethinking legal education. Finally, it explains how the Street Law program meets many of the Reports' objectives. While other clinical, externship, or experiential courses might also advance the Reports' objectives, the Street Law Program is unique in that students learn legal doctrine and practice important lawyering skills mainly through their teaching of the law to non-lawyers.

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)0,010
score de la tête « metaresearch » (Gemma)0,017
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 enseignants0,150
Score d'incertitude au seuil0,299

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,017
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0100,007
Communication savante0,0120,007
Science ouverte0,0040,005
Intégrité de la recherche0,0090,013
Charge utile insuffisante (le modèle a refusé de juger)0,0130,003

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 Opus0,037
Tête enseignante GPT0,361
Écart entre enseignants0,324 · 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

Citations1
Publié2009
Routes d'admission1
Résumé présentoui

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