Making internships meaningful: the challenge of encouraging reflection and skills articulation.
Bibliographic record
Abstract
In recent years considerable emphasis has been placed on embedding employability and skills development in core university curriculum content as well as providing additional support and development to enhance links between learning and work(SFC 2004, 2009, Yorke 2006, Pegg et al 2012) . A focus on ‘graduate attributes’, for example, highlights a commitment to developing skills and abilities across the curriculum and university experience (Hounsell 2010, Barrie 2007, Dacre Pool & Sewell 2007). But there is a risk that this has become so ‘embedded’ that studentsstruggle to identify, articulate and (re)present the skills and knowledge they have developed at university to employers and others. This paper draws on critical insights from the Third Sector Internships Scotland programme to explore the challenges many students have in articulating skills and experience. Over the past three years the programme has offered over 275 internships, received 6500 applications from students and offered bespoke feedback on 1000+ interviews. The research team have therefore had access to an extensive and unique dataset from which to consider the spectrum of student employability needs across the Scottish sector. Through this lens the paper poses critical questions about how universities can / should best support students to identify and articulate skills development and the tools and resources available to facilitate reflection and communication.
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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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".