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Record W2981722395 · doi:10.1080/03069400.2019.1667083

Perils, pitfalls and possibilities: introducing reflective practice effectively in legal education

2019· article· en· W2981722395 on OpenAlexaff
Michele Leering

Bibliographic record

VenueThe Law Teacher · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsQueen's University
Fundersnot available
KeywordsReflective practiceTransformative learningPraxisReflection (computer programming)Lifelong learningEngineering ethicsPedagogyProfessional developmentPsychologySociologyComputer sciencePolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

A number of factors militate against easily and systematically introducing reflective practice in the law school environment. This article is intended for legal educators who wish to develop the multi-faceted reflective capacity of their students. A significant barrier is failing to adequately operationalise the term “reflective practice” to build a common language, appreciation and respect for its power to support professional learning – both instrumental and transformative. To overcome this, I rely on a theoretical framework for reflective practice that includes five domains of reflection that ultimately support praxis. Cultivating reflective capacity in each domain produces unique learning outcomes that cumulatively build actionable professional knowledge and expertise, and support professional formation. Poorly introduced “reflective practice pedagogy” may have adverse consequences, preventing the student from appreciating the generative impact reflective practice has on professional learning and nurturing the capacity for lifelong learning. To overcome this danger, I explore 10 perils legal educators may wish to avoid when introducing reflective practice. The first is not being sufficiently clear about your intent and the pedagogical purpose. The second is not choosing a reflective method or task that is “fit for purpose”. To better calibrate pedagogical approaches, I provide a sampling of the myriad opportunities for enabling reflection in each domain. I briefly explore eight other perils and the possibilities for overcoming them.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.375
Teacher spread0.361 · 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 designTheoretical or conceptual
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

Citations9
Published2019
Admission routes1
Has abstractyes

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