Therapeutic Jurisprudence and Legal Education in Pakistan: A Quest for Innovation in Study of Law to Mend Attitudes of Law Professionals Towards Litigants
Bibliographic record
Abstract
Therapeutic Jurisprudence or TJ is movement in advanced countries whereby the judges and other law professionals through their affirmative action have developed a particular behavioral approach towards litigants in the courtroom which results in therapeutic impact on users of justice system. TJ is to study the healing impact of law, as also of legal process, on the litigants. It has taken various ideas and approaches that were firstly found in medical and psychological fields of study – the most important of which was to study the well-being of the user of justice system, i.e., the litigant. David B. Wexler, Professor of Law at the University of Arizona, USA, is father of TJ along with Professor Winick whose writings and studies resulted in establishing of this new field of study of law. In countries like USA, Canada and Australia, TJ has been adopted in number of courts and most important ones are called Drug Treatment Courts (DTCs) or Problem Solving Courts (PSCs). TJ principles are also in use in family courts and domestic violence courts in these countries. The judges who also write on TJ, among others, include California Superior Judge Peggy Hora, USA and Justice Paul Bentley of Drug Treatment Court, Toronto, Canada. In Australia, Magistrate Dr. Michael King holds Perth Drug Court and has written many articles on TJ and its application in DTCs and to the work of judges and magistrates generally. The paper will provide food for thought to introduce TJ concepts in legal education in Pakistan and elsewhere.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".