7066: Allie Gray Kevern, who tended injured soldiers in Devon and Cornwall
Bibliographic record
Abstract
Allie Gray Kevern (1892-1991) was my grandmother. She helped soldiers returning from the war by working in various camps. She kept an autograph book which has in it autographs and stories of people she helped recover from their injuries. Some pages include little drawings, cartoons and poems. Even though she had no medical background she still helped rehabilitate them and give them love and care much like community work. The soldiers whose entries have been extracted are: 6314 Cpl. F. Potts, 1st Somerset Light Infantry. Pte T Pursey, 1 Somerset L I. 9577 Lance Cpl. E. Higgins, 3rd Somerset L. I. 4244 Cpl R. Grice, 3rd East Lancs Regt. Pte Frayne. R. E. 19th Alberta Dragoons, Edmonton, Alberta, Canada. Freddy Knight, 1st Somerset Light Infantry. R. Forshaw, Pte., 1st East Lanc Regt. Pte Arthur Harris Jepson, 3rd East Lancs. 8918 Pte R Taylor, The Kings Own Regt. 9619 Pte F. March, 1st Somerset L. I. L/cpl Croft, 1st Battalion Devon Regt. F Bailey Sergt, 3rd E Lan Rgt. S. Douglas Sheppard, 2nd Batt. S. L. I. 17483 Pte M M Nullp Clacy [name not clear], 3 East Lancs Regt. R T Cuthbert, R A E. W. H. Rookley, Sapper, R.E. C Harris II Corpl R E 1/1 E. L. Coy. There is one entry by Joyce.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.296 | 0.083 |
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".