Bibliographic record
Abstract
In the current conversation about reforming legal education, one of the constant refrains is that law schools must graduate students who are “practice ready.” Commentators go on to argue that for law schools to produce “practice ready” students, they must expand how they offer experiential learning. One potential way to do that is to expand clinical legal education programs. I worry that law schools (and others) are envisioning clinical legal education as a magic bullet that will solve all of the ills and imbalances present in current legal education. In this article, I demonstrate the unhelpfulness of the phrase “practice ready,” and dismantle the idea that clinical legal education, or any other singularly-focused intervention, can transform legal education. Building from key insights already made in clinical legal pedagogy, I offer an alternative vision of legal education as an ecology of learning, in which law school as a whole is understood to be an interconnected and interdependent system that is dynamic, changing, and in action. I articulate how understanding law school as an ecology of learning can advance innovative changes — both small and large — leading to graduates who have better chances of flourishing in the legal profession.
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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 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".