Closing the Gap between College Students: An Intergroup Dialogue Program to Reach Understanding and Reduction of Disparities
Bibliographic record
Abstract
The goals of the intergroup dialogue program are exploration and collective action. The goal of exploration is to spread awareness by presenting the situation. Several students are not aware that disparities between students exist, so this dialogue provided awareness of the issue. With increased understanding of each other, collective action can be taken. Actions by a small number of students are still steps taken to reduce the gap between the financially supported and not financially supported students. Action can look like spreading awareness of the issue, advocating for policy changes, and more. The structure of the intergroup dialogue program required one meeting that incorporated four stages. The meeting lasted approximately two hours. The first hour incorporated the first and second stages. The first stage served as an introduction session. Several participants did not know each other, but the session helped familiarize everyone. It created a more comfortable environment for a touchy subject. Furthermore, participants began to understand each other’s lives and started seeing similarities and differences. The second hour included the third and fourth stages and served as the turning point because students divulged into their finances. Each student shared how much financial help they received. The third session served as the most polarizing session because of the questions they had to answer. It offers room for several perspectives. The fourth session was dedicated to finding ways to help reduce the gap. Even though they are only college students, they can still take steps to reduce the gap.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".