Bibliographic record
Abstract
I killed a peace dove once. It was spring. I was driving down a stretch of road lined with leftover remnants of apple and cherry orchards not yet bulldozed for new houses, new subdivisions. I don’t know where I was coming from, down that particular road, though one corner of my brain thinks it might have been the hospital, and that I was anxious and strung out from lack of sleep, which is why I didn’t see the dove in the road there, small, grey, invisible against the asphalt. I seem to remember it was early morning, the light just cresting the mountains like consolation, and I don’t know why I would have driven down that stretch of road in the early morning unless it was home from one of those hospital visits. Besides, something in my memory wants to connect the accidental suffering and death of a bird with my own human confrontations with death. But now as I write, I’m not sure it was morning, or even sure about the light. It could have been afternoon or evening, might have been a trip to the grocery store. Vacuum repair. Something banal.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.725 | 0.412 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".