Bibliographic record
Abstract
There has been an age-old battle between reason and emotion, continued from centuries from the perspective of man; and reflected in the clash of religion and science. Simply we try to follow reasonand avoid to follow emotion. But in practice, knowingly and unknowingly we follow emotion. In addition to the most of values are rooted actually in emotion not in reason. In the eco-philosophical writings of Arne naess, this puzzle takes much attention; he tries to discover the connection between reason and emotion. In a branch of Indian Philosophy, known as Advaitism (Non-dualism) the connection between reason and emotion studied and explained very beautifully. Advaitism holds that the ultimate goal of human life is to realize Sarvatmata (Everything is identical with self) and demonstrates a path to realize this Sarvatmata. Following the words of Arne Ness, this Sarvatmata can be conceived as realization of the big ecological self. In the school of Advaitism, a systematic way of thinking is found to realize Sarvatmata. The only method to know the self is making our mind free from impurities i.e. infatuation, aversion, attraction, craze etc. These all are products of our ignorance. As one cannot see his face in a mirror whereon dust is present; equally, given that these impurities are there the self is not known. However, as the dust is removed, one sees his face very clearly. Similarly, as these impurities are removed, one clearly realizes that he is not different from other beings, but identical with them.
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 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".