Introduction to the Non-dualism Approach in Hinduism and its Connection to Other Religions and Philosophies
Bibliographic record
Abstract
In this paper, we introduce the Hinduism religion and philosophy. We start with introducing the holy books in Hinduism including Vedas and Upanishads. Then, we explain the simplistic Hinduism, Brahman, gods and their incarnations, stories of apocalypse, karma, reincarnation, heavens and hells, vegetarianism, and sanctity of cows. Then, we switch to the profound Hinduism which is the main core of Hinduism and is monotheistic. In profound Hinduism, we focus on the non-dualism or Advaita Vedanta approach in Hinduism. We discuss consciousness, causality, Brahman, psychology based on Hinduism, supportive scientific facts for Hinduism, the four levels of truth, and Maya. The four paths of knowledge, love, karma, and meditation are explained as well as the cosmic mind, the subtle body, and Aum. The risks for every path are also explained. Then, we introduce the orthodox and heterodox Indian schools including Yoga, Nyaya, Advaita Vedanta, Vishishtadvaita, and Dvaita. Connections to some other religions including Buddhism, Jainism, Sikhism, Judaism, Christianity, Islam, Islamic mysticism, and Zoroastrianism are analyzed. Finally, we explain the connection of Hindu philosophy with the Greek, western, and Islamic philosophies which include the philosophies of Plato, Aristotle, Plotinus, Spinoza, Descartes, Hegel, Schopenhauer, Avicenna, Suhrawardi, and Mulla Sadra.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".