Discourses on Languages and Identities in Readers' Comments in Ukrainian Online News Media: An Ethnolinguistic Identity Theory Perspective
Bibliographic record
Abstract
This study is a pioneering attempt to apply social and ethnolinguistic identity theories developed by social psychologists Henri Tajfel, Howard Giles, and Patricia Johnson, and Judith Butler’s critical feminist theory of hate speech, to Ukrainian realities. The material comprises nearly 3,000 readers’ comments concerning language issues posted to Ukraine’s leading news website Ukrains'ka pravda (Ukrainian Truth) in 2010-12, and is analyzed through a systematic discourse-historical approach within a critical discourse analysis. Notorious for intolerance, filthy language, and trolling on a mass scale, the comments reflected the language situation in Ukraine from 2010 to 2012, demonstrating linguistic optimism, linguistic alarmism, denial of bilingualism, and historicist, legalist, and laissez-fair discourses. The readers’ comments deny or affirm the authenticity of either the Russian or the Ukrainian language, propose the exclusion or inclusion of the Russophone population in Ukraine, or deny that there are identity differences. From the chosen theoretical perspective, this study testifies to an unequal power status of the language groups, to the cultural hegemony of Russophones and the challenge to this hegemony by Ukrainophones, to mutual othering, and to an abundance of hate speech. Arguably, the use of hate speech assisted in developing and cementing the identities of Ukrainians who connected strongly with either the Ukrainian or the Russian language.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".