Communal reflections on the workplace: Locating learning for the legal professional
Bibliographic record
Abstract
There is an increased public expectation that Australian universities should assume responsibility for ensuring that their graduates are work-ready. Victoria University (VU) in Melbourne has implemented a commitment to Learning in the Workplace and Community (LiWC) which requires that 25 percent of all courses be assessed by situated learning. Workplace training has a long tradition in legal education. The real work environment is seen as basic to the training of legal professionals, through legal clinics, post-degree practical legal training and apprenticeship models such as articled clerkship. In the Bachelor of Laws degree at VU, typical of many law degrees, work placements are often extra-curricular and so have been invisible in terms of measurable learning outcomes or measurable LiWC components of a course. Law in Practice (LiP) is a unit of study that accredits the workplace experience and identifies and assesses the learning that occurs in the legal workplace. We argue that the significance of the 'de-situated' online space for both individual reflection and peer interaction is central to the syncretization of various sites of learning. As well as reporting on the curriculum design of the online resources, the discussion will draw on generalized analysis of student journals to report on student responses to LiWC as a learning experience enhanced through personal and social reflection in online discussion.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".