Elements of a counter‐exhibition: Excavating and countering a Canadian history and legacy of eugenics
Bibliographic record
Abstract
Into the Light, a recently mounted collectively curated museum exhibition, exposed and countered histories and legacies of 20th-century "race betterment" pedagogies taught in Ontario's postsecondary institutions that targeted some groups of people, including Anishinaabe, Black, and other racialized populations, and disabled and poor people, with dehumanizing ideas and practices. This article advances understandings of the transformative potential of centralizing marginalized stories in accessible and creative ways to disrupt, counter, and draw critical attention to the brutal impacts of oppressive knowledge. The "counter-exhibition" prioritized stories of groups unevenly targeted by such oppression to contest and defy singular narratives circulating in institutional knowledge systems of what it means to be human. The authors draw on feminist, decolonial and disability scholarship to analyze the exhibition's curation for the ways it collectively and creatively: (1) brought the past to the present through materializing history and memory in ways that challenged archival silences; and (2) engaged community collaboration using accessible, multisensory, multimedia storytelling to "speak the hard truths of colonialism" (Lonetree) while constructing a new methodology for curating disability and access (Cachia). The authors show how the exhibition used several elements, including counter-stories, to end legacies of colonial eugenic violence and to proliferate accounts that build solidarity across differences implicated in and impacted by uneven power (Gaztambide-Fernández).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".