Bibliographic record
Abstract
Alms & Matter works against the predominance of the tragic mode in queer narratives by taking heteronormative romantic tropes and reapplying them to characters who normally would be offered suffering and strife instead. The novel aims for a queer optimism rooted in a reality that is not, and should not be, limited to tales of queer suffering. The novel’s division into four parts reflects the setting which binds the characters together: a door-to-door fundraising office in Calgary. The protagonists follow a script when soliciting donations, of which Waiting, Rapport, the Ask, and Concern-Handling are fundamental parts. While we follow the characters through their work, we see the main plots go through the same format. The story is set up in Part I, disrupted and developed in Part II, the stakes raised in Part III, and the plot and subplots resolved in Part IV. \n \nSetting the novel in a fundraising office blends oppression with lightheartedness; though the stories the protagonists present are often dire, many of the interactions they have at the door are startling, bizarre, and surreal. Though many of the scenes take place in or around the workplace, and the protagonists talk about contemporary global issues, the plot revolves around the social aspects of their lives. Both of the novel’s primary romantic plots begin with standard tropes of queer suffering, and typically end in tragedy. In Alms & Matter, these tragic tropes are averted.
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 | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.288 | 0.089 |
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".