More and Better Grant Proposals? The Evaluation of a Grant-Writing Group at a Mid-Sized Canadian University.
Bibliographic record
Abstract
Obtaining external funding has become increasingly difficult for Canadian researchers in the social sciences and humanities. Our literature review suggests that grant-writing groups and workshops make an important contribution to increasing both applications for external funding and success in funding competitions. This article describes an 8-month grant-writing group for 14 social scientists in a mid-sized Canadian university. The goal was to increase applications and successes in funding competitions. The group integrated several strategies perceived by Porter (2011b) to encourage more and better grant proposals: offering "homegrown" workshops that were ongoing rather than occasional, sharing successful proposals, coaching and editing, bringing together emerging researchers with established ones, and placing participants in reviewers' shoes. These strategies were combined in a series of monthly sessions that required participants to write each section of a grant proposal and share it with others for feedback. Participants perceived this approach to work well; it appeared to provide useful feedback and examples, and develop a sense of accountability and community. The number of applications submitted for funding increased 80% from the funding cycle just prior to the group (2013-2014) to the funding cycle during or immediately after the group (2015-2016). The rate of success in obtaining funds from internal and external grant submissions increased from 33% to 50% over this same time period. The greatest increase in submissions and success were experienced by emerging and alternative academic researchers. From their program evaluation, authors conclude that grant-writing groups are a useful way to build researcher confidence and commitment to submitting proposals to funding competitions and contribute to success, especially for researchers with limited experience in such competitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".