Andreas Rutkauskas’s Borderline Project: an Intermedial Mapping of the Canadian-American Border
Bibliographic record
Abstract
This article is concerned with the intermedial rewriting of the Canada-U.S. border active in Andreas Rutkauskas’s online Borderline project. In Borderline, the photographer embeds his combinations of short texts and photographs of various border locations within Google mapping tools such as Google Maps or Google Earth, depending on the interface one chooses. The intermedial confrontation of digital mapping with the photographs and associated texts in Andreas Rutkauskas’s Borderline project invites its viewers to explore visually the line between Canada and the U.S. The border which the viewer is invited to travel along takes on infinite guises, sometimes confirming hard-border imaginaries of barriers and border posts but mostly delivering idiomatic glimpses into a border that never ceases to surprise us in its porosity, impermanence and complexities. Beyond the abstract and vertical gaze active at the border and its divisive politics, Rutkauskas’s combinations of texts and images produce knowledge about the lived border in its banal and contested forms. If Borderline undoes the map of the border, as such it constitutes an invitation not only to explore but to perform the border in a countermapping performance which at once confirms and erases the border in its technological guises and offers to decolonize the map and our imaginaries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".