Freedom to hate: social media, algorithmic enclaves, and the rise of tribal nationalism in Indonesia
Bibliographic record
Abstract
Empirically grounded in the 2017 Jakarta Gubernatorial Election (Pilkada DKI) case, this article discusses the relationship of social media and electoral politics in Indonesia. There is no doubt that sectarianism and racism played significant roles in the election and social media, which were heavily utilized during the campaign, contributed to the increasing polarization among Indonesians. However, it is misleading to frame the contestation among ordinary citizens on social media in an oppositional binary, such as democratic versus undemocratic forces, pluralism versus sectarianism, or rational versus racist voters. Marked by the utilization of volunteers, buzzers, and micro-celebrities, the Pilkada DKI exemplifies the practice of post-truth politics in marketing the brand. While encouraging freedom of expression, social media also emboldens freedom to hate, where individuals exercise their right to voice their opinions while actively silencing others. Unraveling the complexity of the relationship between social media and electoral politics, I suggest that the mutual shaping between users and algorithms results in the formation of “algorithmic enclaves” that, in turn, produce multiple forms of tribal nationalism. Within these multiple online enclaves, social media users claim and legitimize their own versions of nationalism by excluding equality and justice for others.
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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 | 0.002 | 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 | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".