Ethics in Evolutionary Learning Models: A Critique of Comparative Perspectives and the Alternative Applied to the Wellbeing of Canadian Natives, Absolute Reality in Social Issues
Bibliographic record
Abstract
Ethics is susceptible to formal study, application and inferences just as any socio-scientific theme is prone to be. Such was the explanation given by Albert Einstein to his friend, Niels Bohr. Yet in the totality of socio-scientific research program for all times, even as far back as Aristotle, the concept of morality and ethics was overshadowed by the epistemology of rationalism. This marked the persistence of methodological individualism or hegemony by governance in the individual and the collective. The Mind-Matter relationship, although desired by so many of the thinkers for organic causality between them, was rendered defeated by the inability to answer the greater worldview of morality and ethics. Thus there arises the new paradigm that this chapter has presented. We refer to it as endogenous ethics of the epistemic grounding in unity of knowledge. The result is also the regenerative and creative nature of the unified reality intra- and inter-systems. This chapter has developed the theory, formalized it in a phenomenological model of unity of knowledge and the world-system. The results of the formalism compared with the ethical theory of Abraham Edel’s Existentialist Perspectives (EP) was applied to the wellbeing theme of Canadian Natives in regards to their philosophy of education and development as a lifelong holism of evolutionary learning with the spiritual roots. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.108 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".