Grant writing training for undergraduate students: contributions to deeper learning and employability
Bibliographic record
Abstract
Applied-based learning pedagogical tools, such as grant writing training, are increasingly recognized as important for the career growth of undergraduate students, particularly in environmental, social science and humanities degree programs. This paper presents research findings from a Canadian university study that assessed the impact of grant writing training implemented in a second-year geography course, covering topics on environment and development. Research findings show that the training enhanced student motivation and academic performance by offering real-world applicability and opportunities for deeper engagement with course topics. Many students also appreciated the practical skills they gained from this activity, such as budgeting and results-based management, which they deemed valuable for future careers in the public, non-profit, and policy sectors. However, some students were unable to fully grasp how grant writing training was related to their studies, which might be partly due to elements of the hidden curriculum. As the development of professional skills is just as important as disciplinary knowledge for employability, we encourage a stronger integration of the two into education curricula as well as efforts to nurture students’ self-awareness and agency to articulate these skills.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".