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Record W7001582509

Learning in Lockdown: Assessing the Impact of Online Legal Education on the Development of Professional Competencies and Identity

2024· article· en· W7001582509 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationLegal educationLegal researchIdentity (music)Legal professionCurriculumSession (web analytics)Term (time)
DOInot available

Abstract

fetched live from OpenAlex

We administered a survey to law students at three Canadian law schools in the Atlantic region at the end of the Fall 2020 term. The survey asked students to compare their learning of each of the Federation of Law Societies of Canada (FLSC)-mandated competencies, as well as several others taken from the Law Society of New Brunswick (LSNB) competency profile, in the Fall 2020 (online) term versus previous terms of study. The survey also solicited testimonies on students’ socialization into the legal community during their unexpected online law school experience. Our data sheds lights on which competencies, among those deemed essential for law school graduates, are less likely to be adequately developed in an online instruction environment. Our analysis shows that online learning is not demonstrably less effective at fostering the FLSC-mandated competencies. Socialization into the legal community was the aspect of the typical law school experience most hindered by the online modality of instruction. While the Fall 2020 term was a difficult experience for many students, we also found that a sizeable minority of students thrived in the online environment, even with the temporary and emergency nature of the online law school experience in Fall 2020 and the pandemic-related public health restrictions during this period. Our findings are consistent with other studies conducted on law students during and before the pandemic outside of Canada. Nous avons mené une enquête auprès des étudiants en droit de trois facultés de droit canadiennes de la région de l’Atlantique à la fin de la session d’automne 2020. L’enquête demandait aux étudiants de comparer leur apprentissage de chacune des compétences exigées par la Fédération des ordres professionnels de juristes du Canada (FOPJC), ainsi que de plusieurs autres compétences tirées du profil de compétences du Barreau du Nouveau-Brunswick, au cours de la session d’automne 2020 (en ligne) par rapport aux sessions d’études précédentes. L’enquête a également sollicité des témoignages sur la socialisation des étudiants dans la communauté juridique au cours de leur expérience inattendue de l’école de droit en ligne. Nos données mettent en lumière les compétences qui, parmi celles jugées essentielles pour les diplômés des facultés de droit, sont moins susceptibles d’être développées de manière adéquate dans un environnement d’enseignement en ligne. Notre analyse montre que l’apprentissage en ligne n’est pas manifestement moins efficace pour favoriser les compétences exigées par la FOPJC. La socialisation au sein de la communauté juridique est l’aspect de l’expérience typique de l’école de droit qui a été le plus entravé par la modalité d’enseignement en ligne. Bien que la session d’automne 2020 ait été une expérience difficile pour de nombreux étudiants, nous avons également constaté qu’une minorité importante de ceux-ci s’est épanouie dans l’environnement en ligne, malgré la nature temporaire et urgente de l’expérience de la faculté de droit en ligne à l’automne 2020 et les restrictions en matière de santé publique liées à la pandémie pendant cette période. Nos conclusions sont cohérentes avec d’autres études menées auprès des étudiants en droit pendant et avant la pandémie à l’extérieur du Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.420
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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