Recommendations for Writing Successful Proposals from the Reviewer's Perspective. (Shop Talk)
Bibliographic record
Abstract
Abstract Creating successful proposals involves two components to accomplish the author's goal support for an important project and communication of the goal to the reviewers. Success often hinges on avoiding common mistakes that distract the reviewers from the best features of the proposal. This article reflects the experiences and advice of three individuals who have been successful in obtaining grant funding and who have also acted as reviewers for funding agencies. Examples of lessons learned are presented in hopes that research administrators will find the information beneficial. Making a Match All funding sources require an application or proposal with similar requirements. Good project ideas have to be well-expressed and must fit into the sponsor's high priority areas for funding. Proposal writing fits into the metaphor of the Diving Contest. First, just like great divers who must be ready to perform according to the rules, well-practiced and informed in their field, and with a strong desire to win, so too must proposal writers' principal investigator (PI) be well-prepared. It is essential to know which project ideas get the best marks, that is part of knowing the rules and recognizing what the reviewers' value. Which of the state-of-the art project ideas is the one that will generate the most enthusiasm? What are the reviewers looking for? Who are the reviewers? And where, strategically, should the most important material be placed? Should it be up-front or should it appear later? The strategic submission of a proposal to a runding agency and the strategic placement of essential information within a proposal are essential parts of creating a competitive proposal. Not all agencies require the same types of applications. For example, proposals to some Canadian agencies are only six pages long. That is the good news. The bad news is that the six pages must describe the entire project, including methods, significance, references, and what die investigators have done in the past. Canadian proposals must be succinct to get the point across. The shorter Canadian applications have the advantage of increasing the feasibility of getting good reviewers because even busy people can read six pages. Reviewers find that the shorter proposals also result in better reviewing because the information in the proposal is more focused and authors do not have space to digress. But shorter proposals also place a lot of trust in the reviewers to determine whether the PI has the skills to be able to do the research since many of the details normally included in a proposal are simply not there. Although proposals to U.S. funding agencies are typically longer than six pages, the principle of writing a focused, well-organized and succinct proposal that follows the agency guidelines is a sound one. The Abstract-idea In a Nutshell A proposal's central idea must connect with the agency and the reviewers. The proposal must represent a novel idea, a new approach, method or tool, or must address a critical issue that has not been well-studied in the past. There must also be a compelling reason to fund it. The importance of the project must be captured throughout the proposal but is especially critical in the abstract. Reviewers who are assigned as first, second, or third reviewers will read the entire proposal but other reviewers may read only the abstract. Therefore, the abstract needs to be carefully crafted. The abstract as well as the body of the proposal must hook the reader with an interesting and well-articulated idea and a feasible plan of study so that all the reviewers on the panel will be able to understand the merits of the proposal. Project Design-A Slippery Slope Reviewers often single out the quality of the project design as the main reason a proposal makes or misses the funding cut. Many proposal writers focus on the literature review and seem to attend less to the quality of the project design. …
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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 | 0.229 | 0.553 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.038 | 0.029 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.043 | 0.021 |
| Insufficient payload (model declined to judge) | 0.105 | 0.132 |
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".