Ecologies of Violence on Social Media: An Exploration of Practices, Contexts, and Grammars of Online Harm
Bibliographic record
Abstract
Violence is an almost ubiquitous phenomenon in contemporary digital environments. In this context, there is a growing need to understand how violence is enacted and represented on social media. Drawing from a case study where Colombian young adults discussed the violence they interacted with on their everyday uses of digital platforms, this article explores how violence on social media is experienced and understood by users. Findings emphasize the need to look at violence on digital platforms as a multifaceted, fluid, overlapping, and interconnected phenomenon. In light of these results, I suggest framing current harmful practices as ecologies of violence. To better explore these ecologies, I outline three specific areas that highlight how violence is transformed on social media: practices, contexts, and grammars. Overall, this study emphasizes the need to recognize and address the complexity of violence in social media—a necessary step toward building cultures of peace in and outside of our digital environments.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".