Bibliographic record
Abstract
This study focuses on the social meaning behind the use of both Ukrainian and Russian in various media texts in contemporary Ukraine. I begin by situating the language issue within the current socio-political context; specifically, I briefly summarize recent language debates relevant to this paper. Secondly, I analyze selected media texts from television programs, films and popular magazines—all instances of the simultaneous and parallel use of Ukrainian and Russian. The analysis is then extended to a discussion of the media’s stake in framing the linguistic situation in Ukraine.The texts in question are approached on the premise that “media usage influences and represents people’s use of and attitude towards language in a speech community” (Bell and Garrett 1998: 3). I consider the media’s choice of language an institutionalized means of framing reality (Popp 2006: 6) and therefore the use of language in the media acts symbolically, creating prevalent ideas about what language can and should do in a particular society (Woolard and Schieffelin 1994, cited in Popp 2006: 5).My analysis of communicative exchange is carried out from the perspective of codeswitching that takes place within a larger social and political context. I address the social dichotomy of “we/they” or what Gumperz (1972) calls “metaphorical code-switching.” My analysis rests also on Auer’s code-switching framework, specifically his notions of “preference-related switching” and “sustained divergence of language choices” (1998b).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".