Structural Models of Titles and Subtitles (on the Materials of the English and Azerbaijani Languages)
Bibliographic record
Abstract
This paper explores the investigation of structural models of titles and subtitles on the materials of the English and Azerbaijani languages. The problem has been studied diachronically at first on the materials of the English language and then these issues have been considered on the materials of the Azerbaijani language. Attempts have been made to explain structural models of titles in the following paragraphs of the article. In this paper a special attention is paid to the expressive devices and semantic variations. Alongside the semantic peculiarities, structural problems associated with the titles and subtitles have been explored as well. Titles and subtitles of the texts as it is mentioned above is studied in diachronic-historical aspect, mainly dealing with the titles and subtitles of the works of art-literature. As to the view of the author this paper is of practical and theoretical importance and which has been substantiated with theoretical-scientific provisions. In the investigation of the problem comparative-typological, diachronic method has been used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".