Bibliographic record
Abstract
In this paper, I analyse Shaheer Tarar’s artwork Jack Pine (2019) to question how settler colonialism is produced and reproduced through surveillant visualisations of the land. Specifically, I explore how Tarar’s representations of surveillant images of the land critically engages with historical and ongoing narratives of white settlement in the Canadian territory. As such, I ask: what knowledges are produced through looking at the land with a surveillant lens? And how does art reveal, trouble, challenge, and resist these knowledges? The underlying premise of my discussion is that surveillance and colonialism are twin logics, that they work in reciprocity to define ownership, extraction, and histories of the land that naturalise white settlement. In centralising Tarar’s art installation as producing new ways of understanding this context, I explore how surveillance art here can reveal the relationship between settler colonial histories and surveillant viewing through how they imagine and represent 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".