Bibliographic record
Abstract
There is a clear public interest benefit for lawyers to ensure access to the rule of law, especially on the part of the vulnerable. This article seeks to show that the seemingly simple relationship between the legal profession and the public interest is in fact more complicated than it looks. Pro bono may be viewed from two perspectives-that of the lawyer and that of the client. From the perspective of the lawyer, the important question is whether there is ethical motivation to engage in pro bono. If, however, the perspective of the client is paramount, then meeting the client's needs is the point of pro bono, irrespective of the lawyer's motivation. Our current approach to pro bono lacks coherence because we embrace both perspectives but seem unable to provide a satisfying account of the existing pro bono policies and programs under either view. Despite this complexity (or, perhaps, because of it, the public interest approach allows both lawyer and client perspectives to inform an understanding of pro bono publico. And, understood in a public interest paradigm, pro bono serves a vital and necessary role in the legal profession and the legal system.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".