Bibliographic record
Abstract
‘To jaw-jaw is always better than to war-war’. Winston Churchill's famous line delivered in June 1954 expresses not only the British wartime Prime Minister's views, but also Bernard Shaw's beliefs about war. Shaw's method of fighting the plague of war was to ‘jaw-jaw’ continually about society's love-hate relationship with war in his plays, essays, and speeches. Shaw wrote many plays that incorporated war as a prominent subject, which is perhaps understandable considering the number of armed conflicts that he lived through. While his war plays – approximately one-third of his dramas – contributed a great deal to his popularity, his polemical writings on the subject often made a pariah of him when his characteristic criticism made him appear unpatriotic and even downright treasonous to the dominant jingoism. Yet Shaw could also support a war when he thought it was just. Perhaps more than any other nineteenth- and twentieth-century writer, Shaw explores the age-old ambivalence of humankind toward war. Himself a ‘bellicose pacifist’, Shaw both understood and deplored society's fatal love affair with violence, and his plays and essays seek to strip war of its sentimental trappings, while reminding his audiences that war is not melodrama. Society's love/hate relationship with war, war as class struggle, and war as reflecting the gender divide – these ideas constitute a recurring theme permeating Shaw's oeuvre. Shaw found his political voice as the preeminent essayist for the Fabian Society. One of seven essayists chosen to defend Fabian principles, he wrote numerous essays on topics ranging from rent to world commerce, gaining fame as the editor of the surprisingly popular Essays in Fabian Socialism , which sold thousands of copies and went through several reprints. Primarily because of the success of this publication, the group chose Shaw to edit the Society's response to the second Boer War, Fabianism and the Empire: A Manifesto by the Fabian Society . The Society published this work despite the lack of consensus among Fabians regarding the second Boer War specifically and the justness of war in general. This manifesto critiqued the merits and demerits of internationalism, a topic that engaged Shaw's interest throughout his long life. Quite early he intuited that patriotism and nationalism fuelled society's ever-readiness for war and that a truly international spirit provided a solution to curbing humankind's tendencies toward aggression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.027 |
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".