Hedging in Newspaper Editorials in the English and Azerbaijan Languages
Bibliographic record
Abstract
The present study has been conducted for the linguistic analysis of hedging, which is meant to be an important linguistic feature expressing tentativeness and possibility. The purpose of the study is to investigate hedging devices in English and Azerbaijan economic and political newspaper editorials and to show the frequently used hedges in these stated languages. Basing on the revealed results, it becomes clear that in English newspaper editorials hedging is observed to be more frequently used. It is necessary to underline that the English political and economic newspaper editorials are seen to be more hedged than the Azerbaijan. The article has been focused on the lexical and pragmatic hedges. Hedges pragmatically are realized to be the markers of politeness in the newspaper editorials in the very languages. The modal verbs are considered to be the lexical hedges, and they have been dealt with from this side in the article as well. It is known that modal verbs are used to express the speaker’s attitude to the reality, and they help the speaker to express ideas indirectly as well. The article highlights the necessity of using the modal verbs in the newspaper editorials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".