Melodic Features of Cause and Result Clauses in Modern English in the Light of Experiments
Bibliographic record
Abstract
Variability of language units is the main feature of a language system and its activity. Variability has distinctive features providing processes like coding, decoding, maintaining and transforming the information from one generation to the other. One of the characteristic features of modern linguistics is the identification of invariants in every level of a language system. It helps us to say that an invariant is the factor that provides the unity of elements but a variant distinguishes different steps of this unity. Variability of language units is the main means providing the communicative function of the language, or “the possibility of variability of language units is the characteristic feature of the language nature”, as G. V. Stepanov says. (Stepanov, 1976, p. 223)Variability is the characteristic feature of the realization of the units of phonological, morphological and syntactic levels of the language in syntagmatics, in other words the units of these levels are represented in variants in the speech act. The study of variability of the sentence intonation can be a great contribution to the development of the variability problem. Our aim is to investigate melodic contour of adverbial clauses of cause and result, the direction of intonation contour, the interval between syntagms and their register.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".