The Interaction of Perceived Subjectivity and Pay Transparency on Professional Judgment in a Profit Pool Setting: The Case of Large Law Firms
Bibliographic record
Abstract
ABSTRACT This paper examines how the interaction of perceived subjectivity and pay transparency in profit allocation is associated with an important aspect of law partners' professional judgment, namely their tendency to accede to the wishes of their client and fellow partner (labeled hereafter as partner accedence). Based on interviews with 56 corporate law partners working in large Canadian law firms, we find higher partner accedence in a less subjective system than in a more subjective system, but only under no pay transparency. We find that pay transparency (versus no transparency) is associated with increased accedence in a more subjective system, but it is marginally associated with decreased accedence in a less subjective system. In an experiment where we randomly assign MTurk participants to conditions, we replicate the finding that pay transparency (versus no transparency) has a more positive effect on partner accedence as subjectivity level increases. Data Availability: Lawyers participated in the study upon which this paper is based only after signing agreements that strict confidentiality of all data would be maintained by the researchers. As such, we are bound by these confidentiality agreements with individual lawyers interviewed for the study. Experiment data from Amazon Mechanical Turk are available from the authors. JEL Classifications: M12; M40; M52.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".