Bibliographic record
Abstract
This is so tragic," Matt mutters as he strains to pick the sticks and seeds out of the remnants of the quarter of pot that he left stashed in the van.Somehow, he and Stacy have made a bong out of an old wax milkcarton, the kind that always reminds me of lunches at elementary school.From my position sprawled out on the thin and dirty mattress that covers the backseat, I consider shifting my head.to examine their handiwork, but give up once I realize that any slight movement would cause me to lose the comfortable spot.It took me forever to stretch myself out in the back of the van so that Mark and I could both see out the window at the countryside around us.We lay on the mattress, like players on Sesame Street awkwardly forming the letter "T" with our bodies.My chin rests comfortably on Mark's giant chest, a gesture which, although I'm sure surprised him, he politely made no mention of.After all, Mark and I usually don't touch.When you choose to be close friends with a member of the opposite sex, there's always that awkward middle territory that you don't want to breach.Touching like this is usually part of the taboo area.But for some reason, tonight it was all right.It was even comfortable.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.240 | 0.088 |
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".