Discoursal Function of Phonological Patterns in Poetic Texts: Implications for (UN-) Translatability of Poetry
Bibliographic record
Abstract
Poetic text, as a literary genre, is distinct from non-literary text because of the special effect it can have on the reader and this special effect in literature-text, as a whole, has been argued to be a function of the special literary forms imposed upon the ordinary language patterns in the literaturetext. Among these special forms, this paper will focus on phonological patterns in poetic texts. Phonological patterns (rhyming, alliteration, assonance, etc.), as special textual tools, can be argued to achieve various special discoursal effects. This paper will analyze a few pieces of poetic text to demonstrate how the phonological patterns employed in them could enhance the textual theme by introducing “defamiliarized” or “dehabitualized” cohesive networks across the text and by reiterating the concepts introduced by the lexical locality of the phonological patterns. The paper will then discuss the implications for the (un-) translatability of poetic texts. Translating poetry does not merely mean producing a text, in TL, which carries rhyming patterns. The TL text should also be equal to SL in terms of the type and degree of special literary effect. For this purpose, the rhyming patterns, due to the special textual function they assume in poetry, should be placed upon the same lexical locality in both SL and TL, a requirement which can hardly be achieved due to the non-isomorphism of sound-meaning relationship across languages.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".