Bibliographic record
Abstract
I. Introduction . . . . . 364 II. Shortcomings of the Existing Bar Exam . . . . . 369 A. The Pretense That the Exam Protects the Public from Incompetent Lawyers . . . . . 369 B. Overview of the Bar Exam . . . . . 372 C. Critiques of the Existing Bar Exam . . . . . 373 1. Problems with the MBE . . . . . 373 2. Problems with the Essay Questions . . . . . 376 3. Problems with the Multi-State Performance Test . . . . . 378 4. Problems with the MPRE and Moral Fitness Screening . . . . . 380 5. Problems with the Weight Given to the MBE . . . . . 380 6. Failure to Screen for Issues Giving Rise to the Public's Complaints . . . . . 383 7. The Exam Hinders the Ability to Create a More Diverse Bench and Bar . . . . . 386 III. Alternative Methods to Measure Bar Applicants' Competence . . . . . 393 A. The First Step: Defining Competence . . . . . 393 B. Computer-Based Testing: An Examination of Other Professions and How the Legal Profession Can Adopt What They Do . . . . . 394 C. The Canadian Model . . . . . 398 D. The Apprentice Model . . . . . 401 E. A Postgraduate, Pre-Admission, Graded Skills-Assessment Course . . . . . 407 F. The Public Service Alternative to the Bar Exam . . . . . 410 G. The Diploma Privilege . . . . . 410 H. Modifications to the Existing Process . . . . . 411 1. Testing Legal Research and Drafting . . . . . 411 2. Credit for Pro Bono Work . . . . . 412 3. Assessing Oral Communication Skills . . . . . 414 I. Beginning the Process of Change . . . . . 415 IV. Barriers to Revising the Entrance Requirements . . . . . 416 A. The Existing Bench and Bar . . . . . 416 B. The Administering Bodies . . . . . 417 1. Cost and Practicality Concerns . . . . . 418 2. Validity and Reliability Concerns . . . . . 418 C. Law Schools . . . . . 420 D. Bar Applicants . . . . . 422 V . Conclusion . . . . . 422
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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".