Bibliographic record
Abstract
Background As more research funding agencies require grant applications to include an array of stakeholder expertise, a clear and well-thought-out Engagement Plan is necessary to ensure grant reviewers understand how projects will engage relevant stakeholders. Objectives Provide a comprehensive framework for drafting Engagement Plans for grant applications that will strengthen research proposals, help to mitigate tokenism and lead to more meaningful engagement in SCI research. Overview This presentation will describe in detail the key components you should include in your grant Engagement Plan, including tools you can utilize to select ideal project partners, develop an activity timeline, choose appropriate engagement methodologies, and prepare an accurate budget. Drawing on established best-practices in meaningful engagement, this presentation will guide delegates through the various components funding agencies expect to see in a thorough Engagement Plan. A novel framework will be shared that addresses common shortcomings in Engagement Plans and provides clear guidance on the elements to include in your grant submissions. Conclusions A detailed Engagement Plan clarifies to grant reviewers the steps you intend to take to ensure meaningful engagement in your proposed work, and will help set your application apart in the highly competitive research funding market.
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 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.689 | 0.433 |
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".