Linguistic Analysis of Literary Narratives: A Different Approach to the Study of Women’s Emigration from Ukraine
Bibliographic record
Abstract
The present study aims to reveal how evaluative meanings shape the depiction of Ukrainian emigration and women emigrants in Ukrainian literature of the early 2000s by employing Appraisal Theory developed within the framework of Systemic Functional Linguistics and subjecting excerpts from the following five novels to an in-depth linguistic analysis: Usi dorohy vedut’ do Rymu by Olesia Halych, Shliub iz kukhlem Pil’zens’koho pyva by Lesia Stepovychka, Ia znaiu, shcho ty znaiesh, shcho ia znaiu by Irena Rozdobud’ko, Hastarbaiterky by Natalka Doliak, and Korotka istoriia traktoriv po-ukraiins’ky by Marina Lewycka. The authors employ various grammatical and lexical items to communicate their assessments of the emigrant women characters and the phenomenon of emigration from Ukraine. Appraisal Theory allows us to identify such linguistic realisations of evaluations and interpret authors’ attitudinal positions voiced or implied in text. This research is significant as the first study of its kind using Appraisal Theory to analyse literary texts written in Ukrainian thus expanding the theory’s reach and relevance. Additionally, employing linguistic techniques when assessing the depiction of women’s emigration and its agents enriches an analysis by providing a detailed and balanced perspective. The findings of this research contribute to the fields of literary studies, linguistics, and migration studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".