Dissent, Distance, Dilemmas: ICTs and the Belarusian Diasporic Social Movement Community
Bibliographic record
Abstract
In August 2020, Alexander Lukashenko's re-election, amid widespread allegations of electoral fraud, marked the continuation of his uninterrupted presidency since Belarus's independence and triggered an unprecedented wave of mass protests in the country's history. In response, Lukashenko's adaptive authoritarian regime unleashed brutal repression and systemic human rights violations. In this context, the diasporic social movement community, leveraging information and communication technologies (ICTs), emerged as critical actors supporting the anti-regime social movement in their origin-homeland. Based on semi-structured interviews with 13 members of the North American Belarusian diasporic social movement community, this paper explores the role of ICTs in facilitating their political actions during the 2020 protests, as well as the factors that facilitated or hindered their participation and use of ICTs. Our study highlights that ICTs facilitated diaspora geopolitics from below by enabling ''social movement community,'' where otherwise disparate diasporic satellite publics converged around the common political goal of overthrowing the Lukashenko regime. However, the diasporic social movement community's use of ICTs was also fraught with ethical and moral complexities, navigating the ''proximity dilemma'' of remote participation and influencing a cause from a distance, while benefiting from socio-spatial privileges in their host country. Furthermore, the diaspora's ICT usage is shaped by the political regime, fear of transnational repression, and the geopolitical positions of both the origin-homeland and host country, as a consequence of adaptive authoritarianism in the Belarusian case. We discuss how CSCW can support decentralised, geographically dispersed diasporic organising with respect to social movements under varying authoritarian constraints.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".