Beyond the Numbers: What We Know — and Should Know — About American Pro Bono
Bibliographic record
Abstract
The provision of pro bono services by private lawyers has become a crucial source of legal assistance to poor clients within the U.S. civil justice system. As other features of the system — particularly federally sponsored legal services — have been in decline over the past quarter century, powerful actors in the profession have mobilized to increase pro bono activity. Within large law firms, there is evidence that this project has been a success, at least measured by the significant increase in the aggregate and per-attorney average pro bono hours provided by the large firm sector in the decade prior to the recession.As a result of a new wave of empirical research, scholars and policy makers now know a great deal about how these vast numbers of pro bono hours are produced. But we know much less about how good they are and what good they do. As civil legal aid and public interest law undergo profound changes, including an increasing role for private sector delivery, we need to know whether growing reliance on private lawyer charity is sensible policy. Much of the extant research, both scholarly and field based, focuses on the amount of pro bono that lawyers generate. Yet, despite over a decade of study, we have little information to answer the question of whether pro bono is an effective or efficient way to provide legal aid or access to justice — however that may be defined.This paper seeks to deepen our understanding of both the content and the impact of pro bono. It is aligned with what we identify as a “New Measurement” movement within field — one that seeks to evaluate the quality, cost, and social impact of civil legal services, as well as the quantity. This paper advances the New Measurement agenda by canvassing both what we know and, more importantly, what we need to know about pro bono service delivery. Our review of the literature points toward broad categories of unanswered questions. Specifically, existing research reveals much about various inputs to the pro bono system (e.g., policies and programs to spur pro bono service) and the resultant quantitative outputs such as hours and participation rates, but little about much else, including quality, distribution across cases and causes, impact on lawyers’ ethics, and impact on social causes. The paper identifies what we see as crucial research needs that result as much from gaps in the questions the field has so far chosen to explore as from limitations of available data. It then outlines a path forward to a research agenda that produces information necessary for effective pro bono policy making that moves us beyond the numbers.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.015 | 0.035 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".