Bibliographic record
Abstract
Aspiring historian Maxine is researching Canadian social policy when she discovers the story of Everett Klippert—the last Canadian man jailed simply for being gay. Maxine becomes fascinated with Everett's case and with discovering the man beyond the headlines, a beloved Calgary bus driver on the downtown route who took care to brighten the day of his passengers, who played on the family baseball team and was everyone's favorite uncle, and who, when he was confronted by police about his sexuality, refused to lie. Inspired and captivated, Maxine interviews people who knew Everett Klippert. She connects with a senior at a local assisted living facility she knows only as Handsome, one of Klippert's lovers and perhaps the only person who can truly illuminate the past. At the same time, Maxine is navigating her own new relationship with Métis comedian Tonya. Legislating Love is a heartwarming play that weaves together past and present in a multi-generational exploration of queer love. It tells the near-forgotten story of one of Canada's quiet heroes and reminds us all that the past must be remembered as we work together for a better future.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".