Intraprofessional Competition and Earnings Inequalities Across a Professional Chasm: The Case of the Legal Profession in Québec, Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".