Multidisciplinary Marty Friedland, miscarriages of justice, and the modern law school
Bibliographic record
Abstract
This article examines the contributions of ML Friedland to scholarship and the modern law school. It argues that his research throughout his long, distinguished, and continuing career has been focused on injustice more than on what is required for justice. It is argued that such an injustice-focused approach is still an important research agenda for academic lawyers. Friedland’s focus on injustice is traced through his work on bail and his true crime trilogy. His work on bail demonstrates the connections between fundamental empirical research and evidence-based law reform. Friedland’s focus was on miscarriages of justice broadly conceived to include both inequalities and the importance of proof of guilt beyond a reasonable doubt. It was not limited to those who can be proven to be factually innocent. The second main point of this article is that Friedland was committed to a broad and pluralistic approach to multidisciplinary that included, but was not limited to, the social sciences and humanities. It differed from the approach taken in the influential 1983 Arthurs report by including the entire university and other professions. Friedland recognized that the practice of law was deeply multidisciplinary, including the important role played by expert witnesses. It is argued that Friedland’s distinct approach to multidisciplinarity could form a basis for more respectful, humble, and intellectually rewarding relationships between law schools, the entire university, and the legal profession.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".