296 Investing in Community-Led Research Capacity Building: New Seed Grant Type
Bibliographic record
Abstract
OBJECTIVES/GOALS: We will describe the community-driven development and impact of the new Community Research Capacity-Building grants from the Alliance for Research in Chicagoland Communities, Northwestern University. Communities expressed that to enter equitably into partnerships with academics they need support to build their own community research capacity. METHODS/STUDY POPULATION: ARCC Seed Grants, since 2008, included Partnership Development and Research Pilots, which are both jointly submitted by a community-academic partnership. The new Community Grants are submitted only by community partners and don’t require an academic partner. These grants, $3,000 over 6 months, support the development or strengthening of organizational or community-level research capacity. This may include assessing community capacity to lead and/or collaborate on research; building research capacity of community organizations (staff, leadership, residents), developing community infrastructure (e.g. research principles; staff research responsibilities; process for assessing/ tracking researcher inquiries; template memorandum of understanding) or community research priorities, etc. RESULTS/ANTICIPATED RESULTS: Eight ARCC Community Research Capacity-Building Seed Grants have been awarded so far as a part of three cycles of applications over 2022-23 (2 in 2022, 6 in 2023). During this time period, data has been collected during the application process, in final reports, and in informal group and individual discussions. Information about the profile of grantees (community representation, health focus, etc.), the initial impact of grants, and feedback from grantees about the positive and challenging aspects of the grants will be shared. Grantees have informally shared that the awards have helped to address concerns that many low-income communities of color have their voices are not adequately included in research and other decision-making. The poster will be co-presented by a community grant recipient. DISCUSSION/SIGNIFICANCE: To ensure that research partnerships are community-driven & equitable, it is necessary to invest in community research capacity-building. More evaluation is needed to understand the grants impact, as well as other approaches to community research capacity and leadership development. Poster will be co-presented by a community grant recipient.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".