Bibliographic record
Abstract
It is striking that several recent Irish short story collections by women writers, including Danielle McLoughlin&s;s Dinosaurs on Other Planets (2015), Mary Morrissy&s;s Prosperity Drive (2016), Lucy Sweeney Byrne&s;s Paris Syndrome (2019 ), Nicole Flattery&s;s Show Them a Good Time (2019), Éilís Ní Dhuibhne&s;s Little Red and Other Stories (2020), Louise Kennedy&s;s The End of the World Is a Cul de Sac (2021), Sheila Armstrong&s;s How to Gut a Fish (2022), Bernie McGill&s;s This Train is For (2022), Jan Carson&s;s Quickly, While They Still Have Horses (2024) and Mary Costello&s;s Barcelona (2024), represent animals as marginal and liminal presences and endow them with disturbing and suggestive properties. Tropes of violence and of Otherness are regularly associated with animal protagonists. Drawing on a range of recent theories of animality and the nonhuman, this essay seeks to consider the way in which the interrogation of and deconstruction of femininity and humanity in these stories are a concomitant of the animal presences in them. Some of these texts question traditional notions of the human and the lines of demarcation between the human and the animal, while others remain undecided about inherited dichotomies that they recognise as detrimental but cannot dislodge. Overall, these stories depict the animal and the feminine as intersecting but also as opposed and conflicted. Trans-species solidarity and the process of becoming-animal lead to a dislocation and rethinking of the feminine, but often the moments of enlarged vision or extended identity come at a cost, are transient or hedged with ironies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".