Bibliographic record
Abstract
In the fall of 2010, and again in spring 2012, the Author taught a course entitled Gender & Criminal Law inside the Western Massachusetts Correctional Alcohol Center in Springfield. Participants in the course included roughly equal numbers of law students from the Author's home academic institution, Western New England University School of Law, and residents of the facility. For fourteen weeks, the class met weekly at the institution to discuss issues including domestic violence law reform, the role of family ties in sentencing, and gender issues in prisoner reentry.The Author taught this course in a modified form of the Inside-Out format. Inside-Out is a national training program founded by Lori Pompa and based at Temple University. It offers training programs several times each year. The program has trained more than 300 instructors to date who have offered 300 Inside-Out courses around the U.S. and in Canada. Most of these instructors are college professors who typically teach undergraduates. The Author participated in training in summer 2009, becoming the first law school professor to join the Inside-Out network.This Essay reflects on the Author's Inside-Out experience. It makes the case for Inside-Out as a particularly useful form of experiential learning for law students. It also describes some techniques she learned through teaching an Inside-Out course that can be implemented in a more traditional law school setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".