Translation and the Circulation of Competing Narratives from the Wars in Chechnya: A Case Study from the 2004 Beslan Hostage Disaster
Bibliographic record
Abstract
This paper examines Russian and English reportage published online over the course of Wednesday, 1 September, the first day of the three-day hostage-crisis that took place in Beslan, Southern Russia in 2004. The material selected for analysis comes from three disparate news agencies: RIA-Novosti , a major, state-controlled Russian news agency; Kavkazcenter , the website of the Chechen armed resistance; and Caucasian Knot , a site founded by Memorial, Russia’s international and historical human rights society. Drawing on tenets of socio-narrative theory that combine narratological tools with the idea of ‘ontological narrativity’ (Somers and Gibson 1994: 38), the paper analyses the construction of the news narratives published by each website, before turning to a comparative analysis of the translated material also published that day. The paper thus contributes to academic knowledge regarding the Beslan hostage-taking and the discourses generated by the event – particularly those produced by fringe groups and in translation – as well as to knowledge of narrative construction as events are still occurring. Preliminary conclusions are drawn regarding the effect of translation on these competing narratives, particularly those circulated by opposition, non-mainstream groups concerning situations of violent political conflict, such as that which still continues in Chechnya and the North Caucasus.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".