Bibliographic record
Abstract
Alec Douglas was a ten-year-old scholarship student at Christ's Hospital preparatory school in West Sussex, England, when the Second World War commenced in September of 1939.In that year, hundreds of thousands of British children were evacuated from cities, ports, and industrial centres likely to be targeted for Nazi aerial bombardment.His mother briefly considered sending him to family in Australia to keep him safe.The larger plan to evacuate British children was aborted when the Battle of the Atlantic began in earnest very soon after the war started.Young Alec would nonetheless make his cross-Atlantic journey in July 1940, though to a different commonwealth.After sailing as part of a convoy to New York, he was welcomed into a Canadian foster family in Toronto.He returned to England in 1943, later resettling in Canada as a young naval officer.Did his early ocean-faring experiences shape his chosen career path?That is a story best left to him to tell, which he does with aplomb in the pages that follow.If Douglas's adult life was adventurous, he had already packed in some unique, even historic, adventures by the age of thirteen, as recounted in these pages.My purpose here is to draw from my own area of interest and scholarship, that of children's history, to contextualize what was undoubtedly-after the early and tragic loss of his father-his most formative childhood experience.The evacuation and resettlement of child refugees between the ages of xii
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.458 | 0.396 |
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".