Intonative Variety of Simple Declarative Sentences in the English Language
Bibliographic record
Abstract
The issue of investigation of structural-syntactic and intonation types and kinds of different sentences in the language, began to be broaden since the third decade of the XX century. The investigation of sentence intonation in the initial stage was more linked with teaching of the languages. In the later period alongside the linguists, psychologists, physics, and specialists of other branches of science also were engaged in the study of speech intonation. Up to the last years, in the carried out investigations, the main attention was paid to the formal investigation of intonation structure of communicative types of sentences. That’s why only the structural-semantic analysis of communicative and derivational types of sentences was not satisfactory enough to discover their semantic contents as a whole. But in the modern stage, study of syntax of sentence and its semantics in the plan of intonation variety, proved that the investigation of this problem is more actual than ever today. Thus, comparative study of sentence belonging to each communicative type, including the study of phono-semantic variety of simple types of declarative sentences in the English language which we carry out in a certain contextual-situational phrase, with intonative variety, bears a special importance in the modern stage. A sentence in a certain situation is used for a special purpose and receives an adequate form of intonation. The same sentence in different contexts is never expressed with the same intonation counters. It is in a certain degree subjected to different variations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".