MétaCan
Menu
Back to cohort
Record W2109754710 · doi:10.1111/1468-0130.00262

Six Weeks at Hawkspur Green: A Pacifist Episode during the Battle of Britain

2003· article· en· W2109754710 on OpenAlexaffabout
Peter Brock

Bibliographic record

VenuePeace &amp Change · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBattleUnit (ring theory)InnocenceService (business)LawMilitary serviceWork (physics)HistoryGeorge (robot)SociologyPsychologyPolitical scienceAncient historyArt historyEngineering

Abstract

fetched live from OpenAlex

In early summer 1940 a small group of pacifist undergraduates from the universities of Oxford and Cambridge had formed a Universities Ambulance Unit. They started training for medical work in June as the Battle of Britain got under way; their training camp, situated at Hawkspur Green near London, lasted for about six weeks. The intellectual caliber of the group was indeed extremely high, though this did not entail necessarily medical efficiency! The unit aimed at providing a service alternative for “unchurched” pacifists liable for military service. Among the campers was Canadian George Grant, later a prominent philosopher, and his letters home provide insight into life at Hawkspur Camp. No camp records exist, but in their old age several ex‐campers have reflected on this “remarkable episode” in their careers. In fact, however, the unit never became a reality. Some ex‐campers eventually joined the armed forces; others engaged in relief work in the London Blitz or some other form of alternative service. The author perceives three significant aspects in the Hawkspur experience: youthful rebellion against war, vanished fellowship, and lost innocence.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0550.010
Scholarly communication0.0050.004
Open science0.0020.007
Research integrity0.0080.020
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.138
GPT teacher head0.400
Teacher spread0.261 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2003
Admission routes2
Has abstractyes

Explore more

Same venuePeace &amp ChangeSame topicHealth and Conflict StudiesFrench-language works237,207