When altruism is remunerated: Understanding the bases of voluntary public service among lawyers
Bibliographic record
Abstract
Abstract The legal profession claims a duty of public service that calls on lawyers to volunteer their time through “pro bono” work (i.e., free legal service). And increasingly law firms strongly endorse pro bono and even remunerate time that is provided to clients without charge. But what happens when pro bono is mandated by the law firm, even compensated? Is altruism undermined? Drawing on a survey of 845 lawyers, we develop an integrated theoretical model to account for how volunteering takes place in the course of legal work. The analysis reveals psychological traits, collective norms, economic exchanges, and organizational dimensions shape lawyers' pro bono work in intriguing ways with marked distinctions emerging when pro bono is remunerated by firms. Collective norms known to foster altruistic behavior appear most relevant to pro bono that is outside the job (i.e., unpaid), while organizational supports and constraints as well as economic exchange factors appear most salient to pro bono that is compensated within firms. We argue that a theory of pro bono work requires a more refined understanding of the forces promoting helping behaviors across several dimensions: whether to help, how much to help, and with or without compensation.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".