Where rookies prevail: Digital habitus and age‐based earnings differentials in online legal services
Bibliographic record
Abstract
Abstract This research investigates how and why the digitalization of work can disrupt age‐based earnings stratification in an occupation. Analysing a service archive dataset from a major online legal service platform in China, the study finds that, contrary to the traditional patterns of income inequality, younger lawyers earn more than older lawyers in the digital legal field. Further analyses of the platform's service records and interviews with lawyers working on this platform suggest that the platform's work content and work distribution mechanism make mature lawyers’ human, social and symbolic capital less useful. Meanwhile, the preferences of platform clients place added value on younger lawyers’ digital habitus and turn it into a new form of cultural capital, manifested in their proficiency and effectiveness in digital communication. By examining habitus and capital in the emerging digital legal field, this research deepens the understanding of the impact of digital technologies on knowledge‐intensive occupations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".