Bibliographic record
Abstract
This article explores the growing presence of digital animation within the work of contemporary visual artists, architects and designers concerned with urban geography. While contemporary theorists such as Bernard Stiegler and Mark Hansen have emphasized the ways in which digital media technologies have colonized cultural memory and foreclosed access to a collectively envisioned future, socially engaged architects and artists have turned to animation as a medium that retains an important aesthetic potential. Digital animation has thus become a primary method for both envisioning alternative urban futures and reconstructing the traumatic past within politically engaged work. This article focuses on four examples, two past and two future-oriented. The conceptual artist Stan Douglas has recently, and uncharacteristically, adopted digital animation and gaming technologies in his Circa 1948 collaboration with the National Film Board of Canada (NFB). The interactive app allows Douglas to re-activate a repressed period of Vancouver’s past, thereby questioning the narratives of progress and property speculation that dominate the contemporary city. The work of Eyal Weizman and the Forensic Architecture project has increasingly involved the use of digital animation techniques to both reconstruct and visualize key dates or events within moments of humanitarian crisis. In the Rafah: Black Friday case study, for example, digital animation and 3D modelling are used to reconstruct a particularly intense four days of bombing during the 2014 Israeli military offensive in Gaza. The artist Larissa Sansour merges live action and digital animation to visually depict bleak and disturbingly convincing Palestinian futures, and the ‘speculative architect’ Liam Young has been employing animation techniques to present urban scenarios that teeter between the technologically utopian and dystopian.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".