Visualising the future: surfacing student perspectives on post-graduation prospects using rich pictures
Bibliographic record
Abstract
The gradual commodification of higher education in the context of an increased focus on graduate employability attributes together with evolving labour markets is creating challenges for universities and students alike. For universities, there has been significant investment in careers services and, through institution-wide initiatives, employability or graduate attribute development established to support graduate transitions into work. Meanwhile, for students, experience of part-time work together with pessimistic post-recession employment discourses are challenging the notion that a good degree guarantees their future career prospects. Simultaneously, decreasing financial support from the state has resulted in worrying levels of debt for new graduates. This pilot study was designed to gain a fresh perspective of how students imagine themselves following graduation. The study used rich pictures (RP) as a methodology to explore student views of life beyond university in the UK and Canada. Content analysis of the RPs provided insights into their thoughts and anxieties about potential challenges for the future. Students presented both positive and negative visions of their future, with success in achieving a respectable performance in their final degree as the key differentiator. The insights gained are discussed in the context of related research into students’ concerns and university initiatives to support students throughout higher education and then into graduate employment. The findings revealed student motivations, hopes and fears which can inform the development of impactful university interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".