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Record 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 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2009
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsnot available
Fundersnot available
KeywordsLegal educationCurriculumBest practiceLegal professionPractice of lawProfessional developmentFoundation (evidence)Legal research
DOInot available

Abstract

fetched live from 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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.361
Teacher spread0.324 · 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 designNot applicable
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

Citations1
Published2009
Admission routes1
Has abstractyes

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