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Even Lawyers Get the Blues: Gender, Depression, and Job Satisfaction in Legal Practice

2007· article· en· W1989260612 on OpenAlexaffabout
John Hagan, Fiona M. Kay

Bibliographic record

VenueLaw & Society Review · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsQueen's University
Fundersnot available
KeywordsFeelingPsychologyJob satisfactionSocial psychologyAffect (linguistics)Power (physics)Clinical psychology

Abstract

fetched live from OpenAlex

It is an intriguing puzzle that women lawyers, despite less desirable working conditions and blocked career advancement, report similar satisfaction as men lawyers with their legal careers. The paradoxical work satisfaction reported by women and men lawyers obscures a more notable difference in their depressed or despondent feelings. Using a panel study of women and men lawyers practicing in Toronto since the mid-1980s, we find at least three causal pathways through which gender indirectly is connected to job dissatisfaction and feelings of despondency. The first path is through gender differences in occupational power, which lead to differential despondency. The second path is through differences in perceived powerlessness, which directly influence job dissatisfaction. The third path is through feelings of despondency that result from concerns about the career consequences of having children. The combined picture that results illustrates the necessity to include measures of depressed affect in studies of dissatisfaction with legal practice. Explicit measurement and modeling of concerns about the consequences of having children and depressed feelings reveal a highly gendered response of women to legal practice that is otherwise much less apparent. Women are more likely to respond to their professional grievances with internalized feelings of despondency than with externalized expressions of job dissatisfaction. That is, they are more likely to privatize than publicize their professional troubles.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.809
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.359
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2007
Admission routes2
Has abstractyes

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