Bibliographic record
Abstract
In my presentation, I will demonstrate the contrast between the pioneer of the Southern Grotesque, Flannery O’Connor, and her famous story “A Good Man is Hard to Find” with Taylor’s use of the gothic in his YA novel. O’Connor adapted her version of the gothic from her predecessors such as Shelley and Poe. But she veers away from the creation of a fantastical monster tradition of the Romantics to drive the focus of the “monstrous” to the very human but harmful behaviors of her characters. Similarly, Taylor’s narrative does away with the over-the-top fantasy of the Romantic tradition and instead chooses to set the narrative in a realistic space with relevant characters. The difference between O’Connor and Taylor is that in O’Connor’s Southern Gothic the setting is the pinnacle of her story while Taylor’s Indigeneity shines through with his humor, traditional storytelling, and orality in his narrative. Differences aside, it is clear that the motive of each text is a call for social reform in O’Connor’s criticism of the social structure of the American South and in Taylor’s criticism of colonization.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".