Bibliographic record
Abstract
As scholars and reviewers of Licia Canton’s The Pink House and Other Stories have pointed out, certain characters recur across the cycle of stories and certain events are retold from different perspectives. This is especially evident, I argue, in the series of stories related to a traumatizing accident in which a woman writer is pinned between two car bumpers. These stories, interspersed throughout the collection, are linked not only by continuities in character and detail but also by forms of discontinuity: breaks in the frame of fiction, non-linear narrative practices, and shifts in time and point of view. In the wake of the accident, the woman writer – who is variously a narrator and a character – confronts writer’s block and the holes in her memory, as well as all the interruptions of daily life. The stories are a study in silence, a silence that is audible at the level of the story cycle, the narration of each story, and the enunciation of each character. By foregrounding gaps in understanding and moments in which the senses say more than words, these stories prompt readers to look for – to imagine – what cannot yet be known or spoken. They point toward states of emotion and ways of making sense of the experience that might otherwise remain locked in silence.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".