Translation and the Circulation of Competing Narratives from the Wars in Chechnya: A Case Study from the 2004 Beslan Hostage Disaster
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it