Exploring Community Outreach Initiatives for Artist-Run Centers: A Case Study Using Anti-Racist Feminist Pedagogies to \nCreate Inclusive Spaces for Knowledge Exchange
Bibliographic record
Abstract
In a city like Montreal, where language, race and class divide the city into visible and not so visible ways and geographical patterns, it is important to analyze the often unquestioned positions of privilege held both by individuals and institutions. The need to create spaces where critical thought and reflection may take place is therefore important. Based on an anti-racist feminist framework rooted in a thorough literature review, I undertook a case study based on action research, to experiment with the possibilities of opening accessible and inclusive spaces for knowledge creation and exchange in a diverse society. \n \nThe evidence presented in this thesis brings together my personal experiences with outreach programming, and the acquired information and feedback from a two day Recognizing Privilege & Oppression Workshop carried out with the board of directors and staff of articule, an artist run community centre. Data were collected utilizing both ethnographic and auto-ethnographic approaches as well as through participant worksheets, recorded notes from the workshops including key points and decisions taken, as well as the centre’s strategic plan documents. \n \nThe research questions addressed are: What changes can artist-run centres implement to be more connected to the communities in which they are located? Are notions of access and privilege being addressed on a continuous basis? How can changes be actualized under budgetary constraints? In what way should curatorial, programming, and display practices be challenged and/or modified? And what can museums and larger civic institutions learn from community run centres?
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".