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Record W4413161480 · doi:10.1177/09646639251366476

Challenges Faced by Lawyers Representing Chinese Immigrants in Canada: Understanding Lawyer-Client Relationships Through Legal Consciousness

2025· article· en· W4413161480 on OpenAlexafffundabout
Qian Liu

Bibliographic record

VenueSocial & Legal Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Calgary
FundersFaculty of Arts, University of CalgarySocial Sciences and Humanities Research Council of Canada
KeywordsLegal consciousnessImmigrationConsciousnessLawSociologyLegal professionLegal servicePolitical sciencePsychology

Abstract

fetched live from OpenAlex

This article examines how the legal consciousness of Chinese immigrants shapes lawyer-client relationships and affects lawyers who routinely represent members of the Chinese community in Canada. Drawing on in-depth interviews and observations, it discusses how Chinese immigrants' emphasis on the approval of their morality from above, tendency to prioritize distributive justice over procedural justice, and their different understandings of the lawyer's roles in the legal system pose challenges for lawyers working closely with them. I argue that Chinese immigrants' dissatisfaction with their lawyers does not necessarily have much to do with the individual lawyer's competency and style; instead, it primarily results from the gap between what Chinese immigrants expect and what lawyers can do in the Canadian legal system. Bringing the collective characteristics of the legal consciousness of members of ethnic groups into the picture, this article aims to serve as a stepping stone for future discussions about relationships between lawyers and their clients within ethnic communities.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.790
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.178
GPT teacher head0.422
Teacher spread0.244 · 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.

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

Citations1
Published2025
Admission routes3
Has abstractyes

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