A framework for multimodal screen analysis: Alphabet choice in Serbian virtual linguistic landscape (2008-2017)
Bibliographic record
Abstract
This article reports on a study of the presence of Cyrillic and Latin alphabets as well as English, Russian and minority languages on the websites of Serbia’s political parties during the 2008-2017 period, with focus on the 2016 election cycle. A case study of virtual linguistic landscape, the paper harnesses virtual linguistic ethnography and what is here termed multimodal screen analysis. The aims of the paper are two-fold. First, it introduces the framework for the multimodal screen analysis as an integral part of the virtual linguistic ethnographic protocol. Second, by applying the framework, the paper aims to account for the links between the use of alphabets and associated linguistic and other semiotic resources on the one hand and ideology on the other. The study shows that script choice is not arbitrary but rather motivated by pragmatic considerations as well as ideological orientations of website owners; patterns emerge showing associations between Cyrillic and certain political positions, and Latin and others, though these associations are not absolute. Keywords: virtual linguistic landscape, multimodal screen analysis, genre digraphia, Serbia, Serbian, virtual linguistic ethnography
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".