Experimental-Phonetic Analysis of Suprasentential Units in the English Language
Bibliographic record
Abstract
The current article deals mainly with the suprasentential units in English and their characteristic peculiarities. Some viewpoints of western, Russian and Azerbaijani linguists are discussed here. One of the important matters discussed here is to distinguish the notions “text” and “suprasentential units”, which was possible owing to the viewpoints and investigations of specialists in this field. To determine “suprasentential units”, some other terms such as, “micro-text” and “macro-text” are discussed here, too. To get a detailed information on “suprasentential units”, phonetic experiment was carried out. The essence of the article is to determine the phoneticparameters of “suprasentential units” in the form of a short text. The experiment was realised at the Institute of Linguistics of the National Academy of Sciences of Azerbaijan. For acoustic analysis of the recorded materials, “Speech Analyser”, “WinCecil”, “PRAAT”, “MacSpeech Lab” programs have been used. In the acoustic analysis of speech signals of the given short text, the valuable “PRAAT” computer program created by the professors of Amsterdam University Paul Boersman and David Veenik has been widely used. “PRAAT” computer program has wide opportunities, such as to hold ossillographic and spectographic analysis of language materials (in our case, short texts), to get indicators of tonal frequency intensity, and length of language materials, etc. The above mentioned computer program provides specialists and learners with the chance of learning speech fragments having the recording time from several m/sec to several hours.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".