Quietly, Loving Everyone: Early Stories
Bibliographic record
Abstract
The stories in my thesis collection, Quietly, Loving Everyone: Early Stories, explore characters at the beginning, middle, and end of relationships; with lovers, friends, family, and themselves. My collection explores a balance between the narrative arc of minimalist fiction with the content of existential-humanist stories, while also blending earnestness with humour. These stories raise questions about the growing concern with the Anthropocene, the shroud of mystery surrounding chronic illness, the rising rate of suicide, and modes of infidelity among polyamory and hook-up culture. In a mode that occasionally borders on the metafictional, this collection explores the potential of narrative, of how it can both shield us from reality, and shape it too. \nIn “Ulcers and Auras,” the protagonist, Purdy O’Connor, reimagines the grieving process and reconciles how little is known about autoimmune diseases with his recent diagnoses, all while daydreaming of a platoon of ulcers taking over the city of Montreal. In “Hotel Viviane,” the titular protagonist takes a road trip with her older sister to visit Race Point Lighthouse, then years later tries to make sense of her sister’s sudden suicide. Underlying all these stories is a sense that something imminent and foreboding is around the corner; yet I hope to create a respite for my readers with moments of levity and keen observation. Despite their flaws, shortcomings, and grievances, you may still come to quietly love these characters.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".