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 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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".