In case of emergency, break convention: A case study of a Human Library project in an art gallery
Bibliographic record
Abstract
Against a backdrop of elitism and exclusion, many public art galleries are adopting educational processes aimed at tackling society’s pressing social and cultural problems. This paper shares the findings of case study of one such project, a Human Library (HL), hosted alongside a portraiture exhibition in an Art Gallery in British Columbia. Findings show how this ‘safe’ space of ‘discomfort’ illuminated stereotyping, bias, and ignorance and the subsequent transformation of perspectives around both people and art. It also provided a critical space of social reflection on the media and gender issues. Yet those who attended were predominantly middle and upper class and female and there was confusion around the link between the portraits, the mission of the Gallery and the HL. However, the Gallery can be seen as an intentional, critical space of adult education, playing a unique role in promoting social justice and change.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".