Bibliographic record
Abstract
The article investigates the rhetoric means that are used to adorn the artistic discourse. Adorning of the speech means to delever the artistic discourse to a reader by using various rhetoric means. This problem involved the attention of the rhetorics in ancient Greek, Rome etc., and they have interesting thoughts about adorning the speech by using rhetoric means. In those times the stated problem was introduced on political, law-court, and other speeches. In modern times this problem is also in the air. As the cognitive linguitics developes the problem that was proposed by the Roma rhetorics are investigated basing on conscious, mind frames. The article deals with the three main factors that can be introduced by a speaker. They are admonition, evocation and inspiration. These three factors have special importance in the artistic discourse. So, to adorn the speech is the main rhetoric means. The author states that the adorned speech gives the reader a special kind of inspiration and attracts the reader’s attention more effectively. Different lexical and syntactical constructions (for example, word order, lexical repetitions, inversion, chiasm etc.) as well as colorful figurative can be used as rhetorical means. The author tries to explain the importance of rhetoric means in the artistic discourse using some of the rhetoric means in the article. Gesture, mimics and others can also help to increase the effectiveness of the speech. Having investigated the problem the author comes to the conclusion using the figurative, rhetoric means the speaker tries to increase the effectiveness of the information. The author claims that if the artistic discourse is attractive, the listener listens to it attentively, believes in it and remembers it easily. To create the figurative speech means that not depending on the educational level everyone perceives the speech and enjoys listening to it.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".