Bibliographic record
Abstract
Lao Tzu's Tao Te Ching is a classic and fundamental Ancient Chinese philosophical, poetic, and religious text that dates back to 4th to 6th century BCE. This text is intrinsic to philosophical and religious thought in Taoism, Buddhism, and Confucianism, and is used as a source of inspiration for artists around the world. Because of its inherent poetic ambiguity, some of its translations have been criticized for appropriating Chinese culture for Western perspectives, while others have seen it as a way of making the spiritual text accessible to larger audiences. In this paper, I compare and examine four different English translations of the Tao Te Ching using linguistic frame semantic theory. I argue that semantic variance occurs in each because semiotics are frame dependent, and meaning changes depending on the cultural and temporal frames both the translators bring through their use of fidelity and license when translating, and that the audiences bring when interpreting the artefact. Although variances are present between source text and different translated texts, the translation of the Tao Te Ching has managed to continue to extend its life and bridge the language gaps between cultures, spreading interpretations of its philosophical teachings, and enriching not only the target language, but also the source text in the process.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".