Bibliographic record
Abstract
My thesis project, titled “The Wasted Land,” primarily takes aim at T. S. Eliot’s seminal work, “The Waste Land.” While Eliot’s project was to alert readers to decay, both cultural and literal in terms of the cityscape, my project will explore the Montreal bar and rave scene in all its decadence and delirium. \nThe stories themselves, primarily narrated in the first person, will follow two narrators through their katabacal journeys into the bowels of Montreal and into the West of Canada. There are no ancestors there, no guides. The stories will take place over one \nsummer, beginning in April and ending in August. The first narrator, Bea, will plunge into a world where cannibalism of women is most obvious: the sex industry. While Eliot moves East towards religion, J’s stories will backpedal in the opposite direction, the West \n(moving to Calgary), following a tradition of Romantic poetry where Nature may be the remedy to the city life and, more broadly, \n existence in the hyper-real 21st century. Of course, both options as solutions are equally tenuous. \nVoice is a key component for my thesis project. Moreover, while Eliot’s work is poetry, the project will take an interest in a prose that is deliberated crafted, borrowing components from poetics such as cadence, alliteration, and metering. The suburban aspects of the project will deal with unpacking familial narratives and ideas of home (nostos). There is an attempt here to “get to the bottom of it,” the “it” functioning in a myriad of ways: the city and the relationships contained within it, the family, the suburbs, the self.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".