‘When They Fight Back’: A cinematic archive of animal resistance and world wars
Bibliographic record
Abstract
Abstract Since humanity is no longer the epistemological, ontological, or moral measure of all things, then (how) should international political theorists rethink animal politics? The archive ‘When They Fight Back’ records incidences of when animals ‘fought back’. It explores ways of conceptualising resistance and the implications of broadening the concept to include non-human actors via three findings: (1) Animal conflicts are everywhere and classifying them as revolt, reaction, and resistance is a creative exercise that encourages reflections about interspecies relations; (2) Most animal/human conflicts are not treated as ‘conflicts’. Instead, they are normalised within a biopolitical discourse that seeks to reduce resistance (characterised as Animal living) in order to promote living (characterised as Human resistance). (3) If excluded, animal resistance finds its way back into literatures via ethical-aesthetic figurations, traces, and desires ‘for’ the Animal. As such, the archive stages a Clausewitzian case of escalation from resistances into total war. In open hostility towards a perceived enemy, animals fight back – and because they fight back, humanism has built its own form of resistance (i.e., politics, ethics, aesthetics, biopolitics, international relations, etc.). I conclude that Human Being (as a form of resistance) must be surrendered if the war on life itself is to end.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".