Embracing a Möbius strip metaphor and a critical reflection framework to explore personal barriers to service-learning
Bibliographic record
Abstract
Over the past thirty years, community engagement has increasingly become an important pedagogical tool for higher education practitioners. One of the most frequently utilized ways of embedding this into course design is through a practice called service-learning which provides students with opportunities to explore, engage, and contribute to the communities of which they are a part. Although service-learning projects and programs have the potential to respond to society's most pressing needs, for some educators, this promise may be hindered by perceived personal and institutional barriers. To explore these barriers, our research group engaged in collaborative ethnography over a two-year period to nurture a space of collaboration, insight, and vulnerability. We explored two macro level categories of barriers – personal and institutional – before identifying four sub-themes within the personal category: life balance, reputational risk, cancel culture, and student reality. Our findings contribute to theory by demonstrating how the use of a Möbius strip metaphor, when viewed through four critically reflective lenses, helps educators to explore and understand the “wholeness” of the intertwining relationships between our perceived internal and external barriers to service-learning engagement. • Barriers to service-learning engagement are nuanced and complex. • Institutional barriers are tied to personal barriers through educator perceptions. • A Möbius strip metaphor is a useful reflective tool for management educators. • Collaborative spaces for shared vulnerable reflection can be generative.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| 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".