Bibliographic record
Abstract
I was born in October 1939, the oldest of three children. Dad was an agricultural economist employed by the U.S. Department of Agriculture; Mom was a stay-at-home mother and part-time school librarian, with a vivid imagination and strong literary and historical interests, and our home was full of books. Since the 1780s, five generations of my father's family had owned and worked the same farm in western Maine, and that was where Dad was born and grew up. My mother's parents immigrated to America from Germany in 1884 and 1890; they met in Montana, worked a homestead there, and eventually became cattle ranchers in Alberta. After 1910, they took up a less rigorous life of farming in Connecticut and along the shore of Lake Cayuga in upstate New York, and it was there that Mom was born.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".