Service-Learning and First-Generation University Students: A Conceptual Exploration of the Literature
Bibliographic record
Abstract
Background: Despite increased access to higher education in recent decades, first-generation (first-gen) university students continue to face challenges with persistence and completion. Recommended responses by universities include exposing these students to “high-impact” educational practices. Purpose: This article examines the potential of one of these practices—service-learning—to address the disadvantages faced by first-gen students. Methodology/Approach: We review the literature on first-gen students and service-learning and offer a conceptual critique of dominant approaches. Findings/Conclusions: Dominant conceptions of service-learning treat first-gen students as a homogeneous, deficient group and reduce learning to an input-environment-output model. We argue for a more conceptually nuanced understanding of the reasons for the cultural mismatch often experienced by underrepresented groups of students. Implications: The conceptual resources offered in this article are intended to help researchers and policy makers undertake research that captures the diversity and richness of students’ lives and leads to more equitable practices.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".