Can Good Character Be Made Better? Assessing the Federation of Law Societies’ Proposed Reform of the Good Character Requirement for Law Society Admission
Bibliographic record
Abstract
The Federation of Law Societies has announced that it will be recommending that law societies move from reviewing the “good character” of applicants for law society of admission to focus instead on applicants’ “suitability” for legal practice. This paper considers whether the Federation’s approach would address sufficiently the problems with the good character requirement as currently administered. It argues that while the Federation’s approach would be an improvement, a better approach would be to eliminate character based review of applicants entirely, or to adopt blanket exclusionary rules to prevent, e.g., admission of individuals with significant criminal records. It also argues that any reform of the good character requirement must be coupled with the introduction of proper procedural fairness for applicants. The current process for reviewing applicant character offends fundamental fairness and contributes to incoherent and unjust decisions in individual cases.
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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 | 0.152 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".