The Impact of Social Network Sites on Youth Political Engagement in Russia
Bibliographic record
Abstract
This thesis argues that social network sites (SNS) do not just reflect already existing patterns of offline political engagement among youth networks, but also transform and augment these patterns, creating patterns that are wholly unique to youth networks operating through SNS.Political socialization through SNS can partly explain the shift in citizenship norms seen among Russian youth, in turn motivating youth toward networked activism aimed at issues of a highly localized and personalized nature.Russian state demobilization efforts have forced opposition networks to reshape and restructure their political engagement so that the political acts are declaratively "apolitical" or so those political acts that may have taken place "in real life" can only be observed online, thereby decreasing likelihood of persecution.Although a direct causal link between SNS usage and anti-regime youth alternative political engagement cannot be drawn, this type of political engagement in Russia is only possible thanks to SNS. "Likes, retweets, and comments confirm that this is not just some narcissism, but that someone is really watching this.And if on another planet only a select few can feel as though they're a star, ours gives this feeling to everyone.It turned out to be easier for us to create a new world, than to conquer television."-Elena Bazina 1 April 2014 "Today, a lulling Instagram has become our cradle, and an updated Facebook feed has become our alarm clock."
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".