Bibliographic record
Abstract
This issue• brings us reflections from far-flung areas of the world: England, Taiwan, Turkey, and China.The URAM project continues to appeal to scholars of highly diverse cultural backgrounds and is now yielding interesting cross-cultural analyses of the search for ultimate reality and meaning that attempt to locate similarities in their approaches.Professor Noel Boulting of the Mid-Kent College of Higher and Further Education, in Kent, England, examines the thought of a countryman, the great philosopher Thomas Hobbes, and his system of 'Motional Materialism'.Dr. John Cheng brings us a ground breaking comparison of St. Thomas Aquinas's understanding of God as 'Truth Itself', and the concept of Qi as presented in the Guanzi' s Four Daoist Chaptei's.From Turkey; Dr. Ismail Latif Hacinebioglu offers us his thoughts on the epistemological bases of religious thought, as written from the Turkish perspective.Finally, Mr. Xianpeng Qiu of the Chinese National Museum of Ethnology in Beijing discusses the importance of shamanism in the culture system of the Oroqen ethnic minority of northern China.To be sure, each of.these scholars brings a very different preparation and a very different method of analysis to the study of ultimate reality and meaning.Yei, interestingly, each paper demonstrates that the quest for meaning In these diverse cultures is the same: the attempt to meld everyday experiences with the human longing for transcendence.•
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.648 | 0.517 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".