Normalization, Universality, Harmony: The Three-Layer Implications of the Golden Rule
Bibliographic record
Abstract
The Golden Rule has long been well-known and echo across the centuries. We can find the similar expressions in many civilizations and religions. Viewing from the various interpretations of and debates on the Golden Rule, it is obvious that the discussion is carried out from three perspectives: firstly, the “law” aspect of the Golden Rule, which functions in forms of moral laws, principles and norms; secondly, the “golden” aspect of the Golden Rule, namely, how to understand its priority and universal significance in moral rules and principles; thirdly, the harmony in the relationships of self-other, individual-individual, human-object and human-nature. The different interpretations from above three perspectives of the Golden Rule in classic theories of moral philosophy facilitate us with rich theoretical resources from , but cause the dilemma, including Christian theology, Kant’s practical reason, empiricism (such as egoism, utilitarianism, sympathetic ethics) and analytic ethics. From the perspectives of practical philosophy, virtue ethics and the Confucian “loyalty and forgiveness” thought, the harmonious relationships of norms and inherent spirit, particularity and universality, self and other, manifested in the Golden Rule could be more justifiably explained.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.064 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".