Crisis of Meaning: Compassion and Community Engaged Learning
Bibliographic record
Abstract
This research paper explores the relationship between compassion, meaning-making, and curricular community engaged learning (CEL) in a university setting. We contend that CEL, as a key form of community-focused experiential learning (EL), can play an important role in helping address the well-being of students, faculty, and staff by providing opportunities to engage in what they consider to be meaningful activities. Our critical examination of this relationship draws on primary research conducted in Winter and Spring 2022 with members of a large Canadian university and its affiliated colleges. This mixed-methods research includes a Qualtrics-based survey with over 2,500 respondents, combined with a series of interdisciplinary, online focus group discussions with faculty, staff, and students. Our findings reveal that many participants have experienced a “crisis of meaning” in their academic and work lives. They thus seek avenues to engage in meaningful, creative, compassionate, and community-focused activities that provide them with opportunities to foster their overall well-being. Participants emphasized the heightened importance of such endeavours after the COVID-19 pandemic as they pursue deeper connections with others and a stronger sense of purpose beyond utilitarian academic or employment performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".