[Review] Anna Barcz. Animal Narratives and Culture: Vulnerable Realism. Cambridge Scholars Publishing, 2017. xii,185pp.
Bibliographic record
Abstract
Anna Barcz’s Animal Narratives and Culture: Vulnerable Realism sets out to answer two related questions: what do animals add when they are realistically included in cultural texts, and what is the role of fiction in particular? As part of the examination of these questions, the book identifies what Barcz terms ‘zoonarratives’ and develops the concept of zoocriticism itself. Barcz explains that a twentieth-century acceptance of what is likely (and not only what is definite) within understandings of realism has allowed increased scope to explore animal perspectives in fiction. The book’s focus on animal vulnerability in particular in one sense seems to narrow the field unnecessarily: texts celebrating animal agency must also be ‘zoonarratives’, and not to pay attention to this could risk reinscribing victimhood. However, Barcz remarks that foregrounding animals’ victimhood within cultural texts can still be a means of challenging it. Noting the rise of ‘traumatic realism’ in the wake of the Holocaust, she addresses the post-war representations of the ‘ultimate victim’ offered by Lyotard and Agamben. She concludes that ‘there are sufficient reasons that enable us to combine animal studies and trauma studies because both grow out of the difficulty of assessing how animals and mute Jews experience violence’ (41). It would have been interesting to see this opening exploration of animal vulnerability engage with existing animal studies texts on the subject also, perhaps especially Marian Scholtmeijer’s Animal Victims in Modern Fiction (University of Toronto Press, 1993) and Anat Pick’s Creaturely Poetics: Animality and Vulnerability in Literature and Film (Columbia University Press, 2011).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".