Creating community in online critical social work courses
Bibliographic record
Abstract
This piece shares a practical participation assignment that emphasizes community-building, activism, collective learning, and contributions. Grounded in critical and inclusive pedagogical approaches, this assignment strives to create and model critical social work practices that require learners to be thoughtful and intentional about their engagement with the community both within and beyond the borders of the virtual classroom. We included this participation assignment in asynchronous online critical social work courses and continue to develop the assignment based on student experiences and feedback. We share the strengths of the assignment, for example, community-building, mutual peer support, countering the isolation that is commonly experienced in online learning, critical self-reflection and self-evaluation, opportunities for student choice, and encouragement of social justice activism outside of the course, and bringing this back into the course in meaningful ways. We also indicate contradictions and challenges of the assignment, for instance, individualistic expectations of participation, resistance from learners to non-traditional assignments, unsettling dominance in curriculum, and the corresponding implications on student evaluations. Lastly, we share our hopeful roadmap for support and implementation of critical and inclusive pedagogical approaches for online social work education and creative assignments that build community inside and outside our learning and teaching contexts.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".