Bibliographic record
Abstract
Museums are frequently sites of struggle and negotiation. They are key cultural institutions that occupy an oftentimes uncomfortable place at the crossroads of the arts, culture, various levels of government, corporate ventures, and the public. Because of this, museums are targeted by political action but can also provide support for contentious politics. Though protests at museums are understudied, they are far from anomalous. Tear Gas Epiphanies traces the as-yet-untold story of political action at museums in Canada from the early twentieth century to the present. The book looks at how museums do or do not archive protest ephemera, examining a range of responses to actions taking place at their thresholds, from active encouragement to belligerent dismissal. Drawing together extensive primary-source research and analysis, Robertson questions widespread perceptions of museums, strongly arguing for a reconsideration of their role in contemporary society that takes into account political conflict and protest as key ingredients in museum life. The sheer number of protest actions Robertson uncovers is compelling. Ambitious and wide-ranging, Tear Gas Epiphanies provides a thorough and conscientious survey of key points of intersection between museums and protest – a valuable resource for university students and scholars, as well as arts professionals working at and with museums.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".