Saving Souls or Saving Money: A Bargain of Conversion in G. B. Shaw’s Major Barbara
Bibliographic record
Abstract
George Bernard Shaw, as a secular writer, in Major Barbara tries to bring about an ethical balance between power and morals. As a matter of fact, life is governed by power factors while morals are masks those taken off in case of emergency. The paper, however, examines the struggle between fanaticism and secularism and the misconception of the exact meaning of bribery and charity.The conflict is based on a bargain between Barbara as a Major in the Salvation Army and Undershaft as ammunitions magnate. The challenge builds on a bet between the two competitors. Either Barbara saves the soul of a capitalist tradesman or the Major accepts saving money. The bargain takes two rounds. The fatal blow is dealt when the Army commissioner accepts, so to speak, ‘tainted’ money.The paper gives a lesson to the in charges not to behave foolishly. They should realistically think to avoid harming a third party. On one hand, Major Barbara rejects the monetary support that is given to the Salvation Army to keep on its reputation while the commissioner, Mrs. Baines, accepts the money to keep on its shelters open. Who is bribed and who is loyal? On the other hand, Undershaft, a secular realist, believes in doing everything to avoid poverty but Barbara, a fanatic idealist, does everything she can to avoid a real disrepute. Who is right and who is wrong?
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
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".