Bibliographic record
Abstract
The Drawn Like Money Series is a group of drawings, made with pen and ink, based on my own photographic images of the Canadian Arctic. The works are intended to resemble the conventional engraved syntax found on paper currency. Drawn Like Money, which was occasioned by a collaborative project entitled Ar t and Cold Cash, is excerpted here to demonstrate my interest in representations of landscape and wildlife through visual rubrics that have come to confer notions of economic value upon pictorial representations. Art and Cold Cash is an art project connecting contemporary art and discourses surrounding money with the development of works in video and by a five-member artists' collective. Jack Butler, Sheila Butler and I are contemporary Canadian artists who have lived and worked in the Canadian North. Beginning in 2004, we undertook an artistic collaboration with writer, Ruby Arngna'naaq, and artist, William Noah. Those two Inuit members of the Art and Cold Cash Collective lived through the change from barter economy to capitalism in Baker Lake, Nunavut, Canada. So, the experience and knowledge of Arngna'naaq and Noah are linked with the perspectives of Jack Butler, Sheila Butler and myself. A key factor in this project is the history of twentieth century Canadian art as concurrent with the relatively recent introduction of capitalist exchange in the Canadian Arctic. As a creative response to the historical conditions around which it is centered, the project is committed to collective art making and analysis as culturally necessary and creatively expansive at this time of increasing globalization. Specific to the Drawn Like Money Series was the idea that nationhood in Canada has in part been forged in relationship to images of the land, including those painted by artists and illustrators whose works were modeled on a British idea of landscape; by the paintings of the Group of Seven; and with regard to other such representations displayed on paper money since the midtwentieth century and earlier. Images of the land have been used to promote Canadian nationalism in a number of ways, including, as Emily Gilbert has shown, by drawing upon the kinds of natural images that have long fed the Canadian imagination. ' Thus the circulation and flow of currency bearing landscapes in Canada has historically been a means to encourage within the minds and hearts of citizens involved in daily capitalist exchange nationalist sentiments tied to representations of the land. …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.021 |
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".