Australian Law Students’ Values: How They Impact on Ethical Behaviour
Bibliographic record
Abstract
This paper proposes a solution for law schools seeking to enhance access to justice in their communities, but with inadequate resources to divert towards fully-fledged clinical legal education (CLE) programs. The solution, it is suggested, is a student lead initiative based on a Canadian model entitled Pro Bono Students Canada (PBSC). Australia boasts a number of excellent educational programs which incorporate a pro bono ethic; however, these are extremely resource intensive and often beyond the reach of law schools grappling with reduced government funding and other budgetary challenges. Confronted with similar resource constraints, Canada has not only recognised the benefits of involving students in access to justice initiatives, but has also taken steps to strategically align them with national pro bono objectives. It is argued that the creation of a Pro Bono Students Australia – a highly visible and formulated pro bono program - would allow both lawyers and law students alike to put the ideals of justice, equity and accessibility into practice.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".