Case Study for a Research Capacity Building Initiative for Community-Based Organizations
Bibliographic record
Abstract
The Research Center in Minority Institutions at Florida International University (FIU-RCMI) is dedicated to rigorous, community-partnered health disparities research and training. The FIU-RCMI's Community Engagement Core's Community Research Enhancement Grant (CREG) program is a unique funding opportunity specifically designed to bolster research capacity within community-based organizations (CBOs). The goal of this paper is to describe the process used to create the CREG initiative, with particular attention to how it was modified to meet the evolving needs of local CBOs. The 2023 CREG funding cycle was restructured based on Cooke's (2005) four-stage Research Capacity Building (RCB) framework. In direct response to the feedback from previous CREG awardees, CREG was reconceptualized as a capacity-building opportunity rather than a conventional grant. This paper contributes to the literature on community-engaged research by highlighting the value of an iterative feedback process involving diverse stakeholders, ensuring ongoing alignment with the dynamic needs of both CBOs and researchers when developing research capacity building initiatives.
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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 | 0.146 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads 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".