The idea of academia and the real world and its ironic role in the discourse on Work-integrated Learning
Bibliographic record
Abstract
Work-integrated Learning (WIL) seeks to bridge the gap between ‘scholastic’ training and work. This study explores the ironic fact that the WIL discourse remains formed by the idea of academia and the real world, an idea that in decisive ways creates this gap. A genealogical discourse analysis of how this idea operates in 79 present and past official documents promoting the Cooperative Education (Co-op) WIL model is used to explore this ironic fact. Two accounts of this idea are dominant in both present and past documents – the deficit account, which merely creates the stated gap, and the collaborative account, which both creates and bridges this gap. I emphasise that the Co-op and other standard WIL models embody and (re)produce the stated idea because they locate ‘scholastic’ training outside the ‘real world’. This separation dates back to scholè – the ancient Greek school that aimed to disconnect ‘school’ from ‘work’. Because WIL has the opposite aim, I argue that this separation is in fact counterproductive for WIL. Finally, I argue that locating WIL in a third place outside university and working life can be a way of avoiding the separation that (re)produces the idea of academia and the real world.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.084 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".