Bibliographic record
Abstract
It was late afternoon in Tusayan, Arizona, one of those glorious days in late November when the sun was still warm with no hint of the coming winter.Jane knew that if she was home in Ohio the day would likely be blustery with soggy leaves clogging the gutters and a chill that she could not escape any time she left the warmth of her house.But here, just a few miles from the South Rim of the Grand Canyon, the weather was still magical.This was a favorite time of hers to travel.With families at home with their children attending to the learning process, it was so much easier to move around.Jane sat at the end of the bar by herself sipping a glass of Pinot Grigio.She thought of Phil and the many trips they made together in their almost forty years of marriage.Of course, they'd taken the three children when they were growing up, sometimes arguing with teachers and principals who did not want the children out of school.The time when the family had gone to Mexico was particularly galling.Here were the three children in a foreign country, charting the weather, trying to learn a bit of the language, absorbing themselves in the culture, and she had to defend the experience to middle school and primary school principals who wanted all students in their seats.She smiled at the memory
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.448 | 0.164 |
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".