Bibliographic record
Abstract
The setting of Alissa York’s 2010 novel Fauna, in the Don Valley and its adjacent neighbourhoods of Leslieville and Riverdale, provides a context for the theme of the persistence of life in the midst of waste and destruction. The realist setting of Fauna demonstrates the way the novel values the local and its specific characteristics, and in doing so suggests the way it seeks to reconcile the opposition identified by Susie O’Brien between ecocritical and post-colonial perspectives in contemporary fiction. At once biocentric, multicultural, and urban, the Don Valley setting undermines the discourses of “‘natural’ belonging that are seen to smack dangerously of colonialist forms of essentialism” because its history as a reclaimed habitat (“naturalized” rather than restored) acknowledges that it is a constructed space rather than a natural wilderness. The novel’s shifting narrative perspective includes animal perspectives along with human and reinforces their interconnection, raising the dodgy question of animal subjectivity and entering into dialogue with the genre of the animal story. But rather than projecting human subjectivity onto animals, Fauna makes an ethical choice to recognize the bodily specificity and precarity that humans and animals share. The flourishing of animal and plant life in the “naturalized” Don Valley provides companionship and recognition for the human characters in the novel who frequent the valley in their struggle to overcome trauma, loss, and abuse.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".