Analysis of the Use of Tenses, Modal Verbs, and Constructions in Polish and English Languages
Bibliographic record
Abstract
The article evaluates sentence construction rules using tenses, modal verbs, and constructions in Polish and English. As we know, language is a national phenomenon characterized by typological features that distinguish it from others. However, due to the influence of history and constant interaction between languages, languages have an increasing affinity. It is manifested in lexical similarities, rules for using tense forms, sentence constructions, and modal means. Despite this, each language has its own grammatical features regarding tense forms, modal verbs, and constructions, complicating its study and translation. This study aims to analyze the rules of sentence construction using tenses, modal verbs, and constructions in Polish and English. Comparative-typological, inductive, and deductive methods were employed to achieve this goal. The analyzed rules and features of sentence formation were examined depending on the verb's tense-aspect-mood system, with specific details on the constructions of main and so-called auxiliary tenses formed by combining components of this system.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".