Electric elephants and the lively/lethal energies of wildlife documentary film
Bibliographic record
Abstract
Amid growing enthusiasm for documentary filmmaking as a more‐than‐human research methodology, particularly in geography, I interject a cautionary reflection on the possibilities and limitations of this medium, emphasising documentary film production's effects on nonhuman life. From one of the first films ever made, in which Thomas Edison electrocutes Topsy the elephant, to contemporary wildlife documentaries that observe the lives and deaths of animals, film's affective potential to electrify, animate or enliven has existed in tension with its reliance on an encounterable, killable and invade‐able animal life. Accordingly, this paper first briefly reviews the emerging context of filmmaking as a research tool for more‐than‐human or animal geographies, focused on the relationship between animal documentary film and affect. I then complicate this lively energy through an examination of the conditions of production for animal film, particularly wildlife documentary, underscoring the speciesism that positions animals as disposable objects. Drawing on historical and contemporary examples as well as my own ambivalent experience making a short research film on wild animals circulating within global live wildlife trade, I show that a reliance on an encounterable animal, and a persistent taking of or impinging on animal life, underpins wildlife documentary film's ability to bring animals to life on film screens. At a time when documentary filmmaking is poised to become a dominant method in more‐than‐human geography and animal studies more broadly, this long history of violence, or film's lethal energies, must be considered more centrally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".