Developing Public Service Hybrid Learning Models Through Community-Engaged Design Education
Bibliographic record
Abstract
In many cases, community-engaged design education or curriculum directed towards social issues, was canceled, or shifted to virtual formats during the Covid-19 pandemic.As access to groups, became limited due to the prioritization of physical health -particularly those who were high-risk -many students were denied the opportunity to learn through public service initiatives.Perspectives, a class that brings together design students with people living in long-term care, reoriented to a hybrid delivery format.The resulting hybrid learning model presented key considerations for community engagement in virtual environments beyond the pandemic.In this paper, student and instructor reflections will be explored in relation to course structure and connections to community.The hybrid delivery of Perspectives allowed for a more agile and community-engaged curriculum which was resilient to emergency situations and adaptable to unexpected circumstances; this created new ways for learning outcomes to be activated in community, such as windows into personal spaces and overcoming geographical boundaries to enable working with remote communities.What initially started to maintain relationships and basic connection with community partners, ultimately resulted in the development and advancement of pedagogical methods and approaches in Design.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".