National Features of Discourse: A Systematic Review
Bibliographic record
Abstract
While linguacultural studies are increasingly present in modern linguistics, the field still lacks clarity regarding its scope and applicable methods. This systematic review synthesized 20 studies on national features of discourse in its various types, selected from the EBSCOhost databases. The general analysis showed that most studies were conducted in Kazakhstan, the Middle East, and Africa, with a marked increase in publications after 2020. The in-depth analysis revealed that proverbial discourse was the most frequently examined type (n = 9), often linked to gender issues, while other types such as familial, education, media, and political discourse received limited attention. The findings demonstrate that English culture dominates both monocultural and cross-cultural studies, whereas Kazakh and Russian cultures remain underexplored. Methodologically, critical discourse analysis and conceptual analysis were most widely used, confirming their relevance for identifying national features in discourse. The review concludes that future linguacultural research should expand beyond proverbial and gender-focused studies, strengthen comparative analyses involving Kazakh and Russian discourses, and diversify methodological approaches. These results contribute to a more comprehensive understanding of national discourse features and offer practical implications for culturally informed language education and cross-cultural communication.
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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 | 0.024 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".