Bibliographic record
Abstract
CANADIAN CARDS.2002 "Citizenship?"Th e navy blue-clad border guard asks from his three by three foot brown box."American" I curtly respond, desperate to fi ll the gnawing at my insides.Hunger.I'm sexually starving.He scans the car, looks at me and continues to read from his clipboard.By the look on his face, I can see that he' d rather I' d stay in America.Preferably with all of the other gay people.His grimace makes me anxious."Purpose of your visit to Canada?" and I wonder about his attitude.I'm trying to decide what pisses him off more, that I make more money in a day than he does in a week, or that I'm gay and in his line."I'm gonna go to the casino, maybe hit up a new club." I know the second the words leave my mouth I shouldn't have said the latter part.Loads of people snake their way through the tunnel every day to go gambling.Not so many people admit to going across to Canada to go bar hopping.As I'm alone in the car, it's pretty clear that I'll probably be drinking and driving."Which club would that be?" he asks and now I'm getting worried.I watch his hands, waiting for him to grab one of the orange stickers and slap it on the windshield.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.415 | 0.160 |
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".