Helping the Me Generation Decenter: Service Learning with Refugees.
Bibliographic record
Abstract
Recent research has empirically demonstrated that young adults today are different from prior generations in their decreased empathy, increased narcissism, and decreased civic engagement. The formative years of young adulthood are a critical period for the development of civic values and civil ideologies, a time when college-age adults need to acquire the experiences and skills to decenter and develop into civic-minded stewards of their communities. Engagement in service learning with individuals unlike themselves, i.e., outgroup members, is the approach we have taken at the University of North Florida to encourage this decentering through service learning engagement with refugees embedded in an honors colloquium during students’ first term in college. We took a three-pronged approach to the assessment of the impact of this service learning engagement. In the first approach, evaluations of student responses to open-ended questions provided evidence of a reduction in their self-centeredness and increases in social empathy and multicultural competence. The second approach confirmed these changes in decentering by showing that honors students who were engaged in more interactive service projects with refugees scored higher on two measures of empathy—i.e., the Basic Empathy Scale Basic Empathy Scale ( Jolliffe & Farrington) and the Toronto Empathy Questionnaire (Spreng et al.)—than did students engaged in less interactive service projects with refugees. In the final approach, evaluations of artifacts from the course suggested that levels of decentering, empathy, and civic action differed for students who had intensive versus superficial interactions with refugees.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".