Bibliographic record
Abstract
Capture is a novel that explores human-animal relationships through the lens of artistic representation. Using free indirect discourse and three alternating points of view, the manuscript follows a wedding photographer, Ros, her husband, Bernard, and her best friend, Sarah, who become entangled in a love triangle after Bernard leaves Calgary to join a multi-level marketing company. Ros, once beholden to the concept of love that kept her engaged in her work, becomes increasingly preoccupied with zoo animals as subjects for her photography; Sarah remembers being photographed in art college while negotiating romantic feelings for Ros; Bernard chases his life’s meaning but falls prey to devastating pro-capitalist doctrines. The novel uses ekphrasis as a mode of reflecting on images—mostly the ones Ros produces—of animals as well as human subjects. The dynamics of looking pervade the narrative, since the zoo, one of the novel’s central settings, is a place that encourages visitors to interact with animals as either “the objects of our ever-extending knowledge” (Berger 257) or the fantasy of an exotic spectatorial experience (Malamud). The dissertation considers how this setting, photography, and fiction frame animal subjects through processes of signification while interrogating the kinds of relationships that might follow from this framing.The critical afterword looks at Capture’s place as a polyvocal realist novel in the twenty-first century, arguing that “post-postmodern” literature has a desire to ethically engage with alterity. It then brings together the work of animal studies critics including Donna Haraway, Jacques Derrida, and Cary Wolfe, zeroing in on questions of seeing and subjecthood in relation to the nonhuman. Connecting zoos to late capitalism, the afterword examines the rise of entrepreneurship and the haunting presence of money in the novel. It also attempts to forge a path between animal studies and race and queerness using Haraway’s posthumanist figure of the cyborg. Finally, the afterword considers Roland Barthes’ and Susan Sontag’s seminal texts on photography to suggest that a kind of ekphrastic writing might help represent animals not as allegorical “stand-ins for human beings” (Huggan and Tiffin 149) but as others worthy of attention, respect, and wonder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.494 | 0.257 |
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".