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Record W2003031791 · doi:10.5539/ells.v2n4p53

Saving Souls or Saving Money: A Bargain of Conversion in G. B. Shaw’s Major Barbara

2012· article· en· W2003031791 on OpenAlexvenueno aff
Azeez Jasim Mohammed

Bibliographic record

VenueEnglish Language and Literature Studies · 2012
Typearticle
Languageen
FieldMedicine
TopicLiterature Analysis and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)FanaticismLawMeaning (existential)SoulReputationBalance (ability)SociologyPhilosophyLaw and economicsEconomicsPolitical scienceTheologyPoliticsEpistemology

Abstract

fetched live from OpenAlex

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?

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.162
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.287
Teacher spread0.276 · 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 teacher head, 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
Published2012
Admission routes1
Has abstractyes

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