Bibliographic record
Abstract
[2] see her but once a year if I live so long. My brother Seth married at Prairie du Sac, & by her had 2 sons & 1 daughter Mamie. The older son Seth taught telegraphy to, & he not liking his stepmother- ran away from Colo. & was years later heard from in N.Y. State & finally called on my brother-in-law, Thomas Woodruff, Spencer N.Y. - His baby brother died at Edinburg, Ill - - Mamie was born in Nebraska-Omaha, about 13 yrs, after you & I taught S.W. of Madison. Then Seth went to Oberlin a year, & his wife to Pr. du Sac, then they taught in N.E. Wis. Depere I think, a year & she died. Seth was continued teaching 2 yrs. longer & married an assistant by whom he had 2 sons & 2 daughters. The older son & younger daughter are now with their mother on farm. [3] 2. The 2nd Son, Earnest,- t[illegible], then a year at Brown - taught in Georgia a year - then his Sister Fidelia graduated at Ottawa - & now both are teaching in same school at South Bend Wash. I think you would enjoy meeting him. He takes learning easily. Was the first to study Hebrew at Ottawa, in a class of 3. Mamie was taught in georgia, & now at Raleigh N.C. - Perhaps you know J. A. Sabin My brother in law - taught at Prairie du Chien, Madison, A[illegible] Baraboo & lots of other places in Wis. & in N.Y. N.J. Ill, [Union?], Kan, S. Decotak, Cal. &c, & is now teaching in Ia. boarding with his son-in-law Ira Roberts at Jeffirson. Roberts a dentist. He Sabin was taught 54 years & counted 20,000 pupils under his charge 03828
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.830 | 0.734 |
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".