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Record W4225355767 · doi:10.5430/wjel.v12n3p228

Good and Evil: A Study of Shakespeare’s Macbeth and Kant’s Religion inside Limitations of Plain Reason

2022· article· en· W4225355767 on OpenAlexvenueno aff
Satyendra Arya, M. Sharma, Sam Raj Nesamony, Shalini Saxena

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsnot available
Fundersnot available
KeywordsGood and evilTabooMoral evilForm of the GoodConscienceEpistemologyPolitical ponerologyPhilosophyHuman lifeTerm (time)LawSociologyAestheticsEnvironmental ethicsHumanityPolitical scienceTheology

Abstract

fetched live from OpenAlex

Good and evil run as threads through society in varying forms, from the moral issues of one society to the taboo nature of what is believed to be on the wrong side of the law in another. Many people make judgements about good and evil based on expectations of human culture and conscience. But the real issue is whether people consider good and evil to be dynamic forces or simply the 'must do - mustn't do' preferences that human lives are ruled by. The author of this paper intends to outline the nature of good and evil as forces that reside within the human experience rather than external protagonists, as in reality anything in creation can ultimately be deemed destructive except from a short-term viewpoint. Negative effects are not possible in a creative universe; otherwise creation would not have occurred. Good and evil are polar concepts that provide psychological tools to respond to the chaotic nature of life experience, yet they make it impossible to reflect on the longer-term implications of individual actions.

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)0.001
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 score0.098
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.052
GPT teacher head0.258
Teacher spread0.206 · 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
Published2022
Admission routes1
Has abstractyes

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