MétaCan
Menu
Back to cohort
Record W4391184347 · doi:10.51644/9781771123693-001

Foreword

2023· book-chapter· en· W4391184347 on OpenAlexaboutno aff
Cynthia Comacchio

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.458
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4580.396

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.

Opus teacher head0.041
GPT teacher head0.293
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicWorld Wars: History, Literature, and ImpactFrench-language works237,207