A lucky draw? Theorising how work placements develop diverse university students’ career stories
Bibliographic record
Abstract
Universities can prepare students for work, and universities can educate increasingly diverse student cohorts, but can they do both concurrently?This question of whether universities can offer equitable and inclusive careers education is increasingly under scrutiny.In this study, we address the largely under-theorised area of work-based placements from the perspective of career identity formation for diverse students.We do so through the adoption of Meijers and Lengelle's theorisation of 'career stories' which position the narrative as the mechanism to understand how students' have developed their career identities and future professional goals.Drawing on longitudinal interviews with disabled students, we explore university placements as 'boundary experiences' which can either enable, or disable, the formation of students' professional selves.Our findings indicate a troubling amount of variability, and indeed, luck within the placement offering, often unsupported by intentional pedagogical design.This suggests that the current university placement experience does little to support the professional identity formation processes of diverse students.Through this study, we further translate a processual learning theory from career learning to support future intentional pedagogical placement design in the university context for diverse students.The article ends with a consideration of how placement experiences can better align to equity goals of the university, and provide scalable, high-quality learning experiences for all 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".