'It is more complicated than you first think!' The challenges of work-based practica
Bibliographic record
Abstract
Work-based practica (that is practica in the student’s usual place of employment) can be viewed as an ‘easy option’ for locating placements in the context of high competition for a limited range of practica opportunities. Students who are currently working in the human services sector as well as mature age students with family/life responsibilities also see that work-based practica (WBP) is a pragmatic and equitable way of reducing the pressure of study/work/family demands. It can also increase access to a professionally based award with a huge practica load. However we would argue that a WBP is a complex option with significant advantages and disadvantages and, before undertaken, university educators need to explore the complexities. This article presents findings on the third and final stage of a project that explored current practices, concerns, advantages and disadvantages of WBP in social work from the experiences of field-based supervisors and students who undertook WBP during their course. This stage builds on the previous two stages of this project that explored the practices, concerns and advantages and disadvantages of WBPs from the perspective of university-based educators in Australia, New Zealand, Canada, and at an international meeting of social work educators. Several recommendations are proposed if WBP is to become a viable option for students.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".