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Record W4416912127 · doi:10.1080/03098265.2025.2593483

Grant writing training for undergraduate students: contributions to deeper learning and employability

2025· article· en· W4416912127 on OpenAlexafffundabout
Helena Shilomboleni, Farah El-Shayeb

Bibliographic record

VenueJournal of Geography in Higher Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsEmployabilityTraining (meteorology)Higher educationGraduate studentsUndergraduate educationWork (physics)Grant writing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.123
GPT teacher head0.551
Teacher spread0.428 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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