The Grammatical Ways of Expressing the Future in English and their Corresponding Forms in Azerbaijani
Bibliographic record
Abstract
Belonging to different language families, the English and Azerbaijani languages differ in all the aspects (grammar, phonetics and lexis) of the language. Therefore, as non-native speakers, Azerbaijanis have many difficulties in learning English. Many scholars try to eliminate those difficulties by comparing and analyzing the languages, finding out the similarities and differences between the languages compared. One of the main problems for Azerbaijani learners of English is learning the ways of expressing futurity in English to be able to select proper means of expression while translating from English into Azerbaijani and vice versa. The development of linguistics in the last few decades has been so quick and manifold that a new insight has been implemented concerning the current problems. It gave rise to the development of the comparative typological investigation of non-kindred languages. We shall try to investigate future tense in English basing upon quantitative typology that investigates this or that phenomena existing in two compared languages. The aim of our investigation is to show the grammatical ways of expressing the future in contemporary English, reveal similarities and differences between the ways of expressing future in English and Azerbaijan and, consequently, provide corresponding forms in Azerbaijani. As English is much richer in the ways of expressing future action than Azerbaijani, we will analyze and provide all the possible ways of conveying them in Azerbaijani. There are many controversial and quarrel some points concerning the future tense problem in English and Azerbaijani. The article highlights these problems by providing prominent linguists’ theoretical points of view as well as the author’s own analysis and approach to the stated problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".