Rendering Visible: Animals, Empathy, and Visual Truths in The Ghosts in Our Machine and Beyond
Bibliographic record
Abstract
The cultural politics of visibility are complex and contradictory when it comes to nonhuman animals. To help interrogate and unpack the challenges, this paper concentrates on documentary film, and particularly Canadian filmmaker Liz Marshall’s The Ghosts in our Machine (2013).Ghosts follows the aesthetic politics of the film’s primary human subject, photographer Jo-Anne McArthur, as she conducts a campaign of guerrilla espionage and compiles a vast photographic record of the largely invisible suffering inflicted on a wide range of animals. As a result, the film develops through an interwoven helix of two visual media: filmmaking and photography. The meaning of sight is a visual trope in the film that not only serves to confront the viewer with McArthur and Marshall’s visual record of animal cruelty, but also as a lens that encourages viewers to recognize interspecies (in)visibilities beyond the screen and the necro-economic foundation of contemporary capitalism. I use this film as a window into the cultural politics of sight, and as a way to illuminate the challenges and possibilities of fostering interspecies empathy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".