Student internships bridge research to real world problems
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate whether unstructured graduate student research internships conducted in collaboration with community agencies build capacity and knowledge for students and community. Design/methodology/approach The paper reports the results of four semi‐structured interviews and 20 pre‐ and post‐internship surveys of students' perceptions of their internship activities; whether participation built research capacity in students and community resulted in the creation of new knowledge and promoted ongoing partnerships and relationships. Findings Students reported generating concrete outcomes for community partners, the acquisition of new research and professional skills, plus an increased understanding of theoretical knowledge. Many students also maintained ongoing relationships with their organizational partners beyond the terms of their internship. Research limitations/implications Limitations to this study are the relatively small sample size and reliance on self‐report measures. Practical implications The paper describes a model for student‐community engagement that benefits both community and students. Social implications As universities explore their relationships with their local communities, graduate student internships 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. These students may go on to be community engaged scholars, or research trained personnel in the community. Originality/value The results presented in this paper demonstrate the 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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".