Yes-no Questions and Wh-questions in English and Albanian Spoken Discourse: Focus on Political Debates
Bibliographic record
Abstract
This paper aims to draw reliable conclusions regarding yes-no questions and wh-questions in English and Albanian spoken discourse. The study outlines the differences and similarities in these question types between the two languages.The corpus of the study includes seven Meet the Press (NBC News) interviews (totaling 2 hours and 51 minutes) with the former President of the USA, Donald Trump which contribute to the English spoken corpus. Additionally, there are two Rubikon (KTV) interviews (totaling 2 hours and 51 minutes) with Hashim Thaçi, the former President of the Republic of Kosovo that are part of the Albanian spoken corpus. A qualitative method is employed to compare, analyze, and draw conclusions based on the findings of the conducted research. The study finds that the English yes-no questions feature the inversion of auxiliary and modal verbs to the subject position. However, the Albanian yes-no questions are characterized by the interrogative particle ‘a’, which may be positioned as the initial or final element or be absent altogether. The results also offer compelling evidence of the differences in the number of wh-words used to ask wh-questions in English and Albanian. They also highlight the most frequently used wh-words and the respective functions of wh-questions in both English and Albanian.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".