Bibliographic record
Abstract
“Trying For The Kingdom” is a novella exploring the themes of queer culture and communication, the coded languages our communities have utilized to seek each other out across time, and the circumstances which made coded communication necessary. Set in the spring of 1987, the story follows Daniel McBride over the weekend of his uncle’s funeral, as he uncovers three interwoven mysteries: what happened the night St. Jude Catholic Church caught fire, where Dorothy McBride vanished to when she crawled into her family mausoleum decades ago, and what his relationship with ex-boyfriend and suspected arsonist Lachlann Mills means to him in light of these revelations. All three mysteries culminate in a series of posthumous ciphers left to him by his Uncle Arthur, the priest of the local parish. In this thesis, the concept of ‘queer coding’ is utilized in the language queer people have created throughout history to safely connect and form communities. Daniel’s struggle to engage in the decoding of these ciphers reflects his reluctance to engage in his sexual identity in light of his religious family and the ever-watchful eyes of his small home town in rural Ontario. The thesis title and chapter titles are inspired by songs from The Velvet Underground. Other allusions include the poems of Sappho, the Sherlock Holmes stories by Sir Arthur Conan Doyle, and The Wizard of Oz.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".