Victoria University learning in the workplace and community: connecting partners, connecting fields, connecting learning
Bibliographic record
Abstract
This paper outlines the innovative cross-discipline Learning in the Workplace and Community (LiWC) partnership model being trialled in the Faculty of Arts, Education and Human Development (FAEHD) at Victoria University (VU). The multi-faceted model responds to challenges arising out of a VU commitment to 25 per cent LiWC assessment across all courses. The model is based on the creation of holistic dialectical partnerships with external organisations in triangular learning relationships, consistent with the reconceptualization of twenty-first century learning. It responds to the challenges of developing and articulating authentic learning outcomes across a diverse faculty and scaffolds quality outcomes in scholarship of teaching and learning, graduate capabilities, flexible learning, and curriculum internationalization outcomes. The multi-faceted model also supports all stakeholder learning and maps learning outcomes, which supports the evaluation of progress. Lastly, this paper will also outline the operationalization model, which addresses resourcing issues, such as workload and time constraints, for all stakeholders.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".