Training Lawyers, Cultivating Citizens, and Re-Enchanting the Legal Professional
Bibliographic record
Abstract
Law schools ought to have a vision for how they contribute to the public good. This article identifies two views of how public value might fit into the mission of the law school. The additive view holds that pursuing public value (cultivating “citizens”) and training “lawyers” are distinct objectives. This view underlies traditional claims that the law school should be housed in the university, and also accounts for the historic tension between academic law schools and the profession.By contrast, the integrative view holds that training lawyers and cultivating citizens are mutually reinforcing. This view inheres in the desire to ennoble the concept of professionalism, an old tendency that is presently in ascendance. A law school that embraces professionalism can place public value at the core of its mission, deploying its internal incentive structures in the service of the public good. However, the concept is at risk of becoming diluted or being imperfectly translated into practice. Furthermore, a sole focus on professionalism may marginalize or exclude certain conceptions of citizenship.To optimize its public value, the law school that embraces professionalism should take pains to ensure it retains its robust meaning. It can do so by locating discussions about public purpose in the privileged parts of the law school, and by investing in pedagogical innovations that truly integrate conceptions of “citizen” and “lawyer.” These efforts should be supplemented by innovations that promote diverse conceptions of the citizen that do not fit cleanly into the rubric of professionalism.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".