Talking Back to the West: \nContemporary First Nations Artists and Strategies of Counter-appropriation
Bibliographic record
Abstract
Over a twenty-year period, renowned artists such as Edward Poitras, Robert Houle, Jim \nLogan, Kent Monkman, among others, appropriate renowned colonial landscape \npaintings and art historical canonical works, and then alter them to include First Nations \nnarratives, as methods of critiquing the exclusionary nature of grand colonial narratives \nand their associated historical, art historical and, by extension, anthropological \ndiscourses. Using counter-appropriation as an artistic strategy, they critique: the West’s \ndisregard for First Nations histories in North America; Art History’s past failures to \nclassify their art objects as Fine Art; and contemporary cultural constructions of \n“Indianness” originating from colonial history and ideologies about the “Vanishing \nRace.” With their works, the artists offer their viewers insight into First Nations histories \nand stories, thereby enriching the multiple narratives and pluralist discourses existent in \nNorth America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".