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Intraprofessional Competition and Earnings Inequalities Across a Professional Chasm: The Case of the Legal Profession in Québec, Canada

2009· article· en· W1536047447 on OpenAlexaffabout
Fiona M. Kay

Bibliographic record

VenueLaw & Society Review · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsQueen's University
Fundersnot available
KeywordsEarningsCompetition (biology)RivalryInequalityHuman capitalCapital (architecture)Diversity (politics)Legal professionPolitical scienceLawSociologyBusinessAccountingEconomicsEconomic growthEcologyGeography

Abstract

fetched live from OpenAlex

Intraprofessional rivalry has a long history. This article examines earnings disparities as a dimension of intraprofessional competition among avocats and notaires in the civil law system of Québec, Canada. Drawing on two large-scale surveys and in-depth interviews with legal professionals, I examine three competing perspectives of earnings inequalities: human capital, social-symbolic capital, and organizational-structural explanations. Through this analysis I seek to examine whether similar causal processes shape earnings across the two spheres of legal practice in Québec. The findings of this study clearly demonstrate that these two professional groups are equipped with differential stocks of capital, and conversion rates differ drastically. Avocats receive greater exchange on their investments in human and social-symbolic capitals. These disparities are most pronounced in sectors of the profession where jurisdictional frictions abound: among notaires and avocats working as solo practitioners and in small firms within competitive urban contexts. The article concludes with a discussion of theoretical extensions and future directions for the study of legal professionals in civil law systems and blended jurisdictions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.346
Teacher spread0.325 · 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 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

Citations9
Published2009
Admission routes2
Has abstractyes

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