Uncomfortable Stories: Canada's TRC and Kent Monkman's Hanky Panky and Welcoming the Newcomers
Bibliographic record
Abstract
In 2019, Kent Monkman’s much-lauded Welcoming the Newcomers was unveiled at the Metropolitan Museum in New York City. A year later, Monkman released Hanky Panky online, inciting a polarized reaction facilitated by social media and the press. This research reframes Monkman’s practice as a storytelling methodology, examines the public response to Hanky Panky–a major departure from the response to Welcoming the Newcomers–and unpacks the work’s controversies. I argue that although the work has proven to be controversial, it is a victory for Canada’s Truth and Reconciliation initiatives. Through Monkman’s practice, Indigenous storytelling provides new insights, and the critical engagement with works by Indigenous artists that has been sorely lacking in Canada emerges full force in response to Hanky Panky.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.072 | 0.026 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".