Bibliographic record
Abstract
At a moment when the discipline of Canadian art history seems to be in flux and the study of Canadian visual culture is gaining traction outside of art history departments, the authors of Negotiations in a Vacant Lot were asked: is "Canada" - or any other nation - still relevant as a category of inquiry? Is our country simply one of many "vacant lots" where class, gender, race, ethnicity, and sexual orientation interact? What happens to the project of Canadian visual history if we imagine that Canada, as essence, place, nation, or ideal, does not exist? The argument that culture is increasingly used as an economic and socio-political resource resonates strongly with the popular strategies of "urban gurus" such as Richard Florida, and increasingly with government policy. Such strategies both contrast with, but also speak to traditions of Canadian state support for culture that have shaped the national(ist) discipline of Canadian art history. The authors of this collection stand at the multiple points where national culture and globalization collide, however, suggesting that academic investigation of the visual in Canada is contested in ways that cannot be contained by arbitrary borders. Bringing together the work of scholars from diverse backgrounds and illustrated with dozens of works of Canadian art, Negotiations in a Vacant Lot unsettles the way we have used "nation" to examine art and culture and looks ahead to a global future. Contributors include Susan Cahill (Nipissing University), Mark A. Cheetham (University of Toronto), Peter Conlin (Academia Sinica, Taipei), Annie Gérin (Université du Québec à Montréal), Richard William Hill (York University), Kristy A. Holmes (Lakehead University), Heather Igloliorte (Concordia University), Barbara Jenkins (Wilfrid Laurier University), Alice Ming Wai Jim (Concordia University), Lynda Jessup (Queen’s University), Erin Morton (University of New Brunswick), Kirsty Robertson (Western University), Rob Shields (University of Alberta), Sarah E.K. Smith (Queen’s University), Imre Szeman (University of Alberta), and Jennifer VanderBurgh (Saint Mary’s University).
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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.052 | 0.017 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".