Good and Evil: A Study of Shakespeare’s Macbeth and Kant’s Religion inside Limitations of Plain Reason
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".