Experimental-Phonetic Analysis of Suprasentential Units in the English Language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it