Bibliographic record
Abstract
It possible that I'm an angel. No, not possible, really likely. After all I'm black. Blacker than most African. An' I born in these Americas. Black like night, like a kinda velvet, an' in my secret places, I got this dark musty pink like those rare orchid. Three, four hundred years of clean, pure blood. Is not that I want to sound like Nazi. What it mean is that we aint had the opportunity to enjoy the advantage of ravage: You 'complish anything is because you half-white, otherwise you black. A little bit like Ben Johnson: Canadian win gold medal; Jamaican found guilty of drug taking. Instead everybody want to keep we down. In we place. Is so some of we does smile a lot. But it have compensation. Is not possible to confuse who you really is. In my family a lot of we take the opportunity to be mostly happy in weself, respectable, polite, hardworking. But we learn early to talk we talk. An it seem like each generation we does grow more beautiful. Cheek bone higher, hair thicker and more curly, neck longer, head perch right on top, small and round. Every bit a we the right size, 'cept the eyes. They getting larger, blacker, deeper. Way way back from them fort in Ghana, from them ship, we could see what pass next. Is how we never there when massa come.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.646 | 0.416 |
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".