Beyond the Numbers: What We Know — and Should Know — About American Pro Bono
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».