Bibliographic record
Abstract
Social practice arts is a growing field and from the University of Auckland to Queens College in New York, one can circle the globe with various forms of social practice programs being offered to aspiring socially-concerned and creatively inspired students. But how are these programs actually preparing artists for the political, cultural, and ethical issues at the heart of a community-engaged practice? This chapter considers the ways in which social practice art is being taught in post-secondary education with an emphasis on ethical considerations. Using the Social Practice and Community Engagement (SPACE) minor at Emily Carr University as a case-study, this chapter will critically examine the three core courses (Ethics of Representation, Social Practice Seminar, and Community Projects) that the program maintains will provide “the ethical frameworks for community engagement that will assist (students) through internships and external partnerships of all kinds.” In considering these partnerships, issues of community engagement fatigue will also be explored. In Vancouver, one of the communities that suffers from this type of fatigue is the Downtown Eastside (DTES). The DTES experiences the highest population of unhoused people in Vancouver. It is also an area that has historically experiences a plethora of pilot projects, research studies, community surveys, outreach centres, and numerous other short-term engagement efforts that, while may have been well intentioned, have left its local citizenry weary. These ongoing phenomena inspired a collaborative effort between Simon Fraser University and local residents to change the ways in which the DTES might be engaged. The result was, Research 101: A Manifesto for Ethical Research in the Downtown Eastside. This manifesto serves not only as an entry point for working with a specific community, but also as a consideration for how aspiring social practice artists might establish an ethical foundation for a live-long career.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".