Student internships bridge research to real world problems
Bibliographic record
Abstract
Purpose The aim of this paper is to report on student perceptions of 24 graduate student internships funded in 2007‐2008 by York University's Knowledge Mobilization (KMb) Unit. These internships provided opportunities for students to engage in research with community agencies around real world problems. Design/methodology/approach The principal sources of data were semi‐structured student interviews, conducted as part of an overall evaluation of the unit by an evaluation team, and student responses to surveys administered by KMb staff. Findings The significant findings were that students reported acquiring research and professional skills, plus a new understanding of theoretical knowledge, and that projects generated concrete outcomes for their community partners. Several students maintained ongoing relationships with their organizational partners beyond the terms of their internship, creating opportunities for ongoing benefits to both students and community partners. Students also identified areas of potential improvement, notably, there is an opportunity to strengthen the experience through integration into a formal curriculum. Research limitations/implications Limitations to this study are the relatively small sample size (n=20) and reliance on self‐report measures. Practical implications As universities explore their relationships with their local communities, graduate student internships appear to have tremendous potential for supporting research and knowledge‐based needs of local communities, while providing valuable skills and training to a cohort of students in bridging academic research to real world solutions. Originality/value This article makes an original contribution by focusing on benefits to graduate students in scholarship of engagement programs that prioritize true partnership between students, universities and communities.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".