Navigating the Road to Success: Guidelines for Preparing Competitive Grant Proposals
Bibliographic record
Abstract
Purpose - Difficulty in securing research funding has been cited as one barrier to the involvement of more librarians and information professionals in conducting original research. This article seeks to support the work of librarians who wish to secure research funding by describing some key approaches to the creation of successful grant applications. Approach - The authors draw on more than 15 years experience in supporting the development of successful research grant proposals. Twelve grant-writing best practices or ‘key approaches’ are described, and a planning timeline is suggested. Conclusions - Use of these best practices can assist researchers in creating successful research grant proposals that will also help streamline the research process once it is underway. It is important to recognize the competitive nature of research grant competitions, to obtain feedback from an internal review panel, and to use feedback from funding agencies to strengthen future grant applications.
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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 | 0.361 | 0.459 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.036 | 0.026 |
| Open science | 0.016 | 0.022 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.023 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".