Unity Starts with U: A Case Study of a Counter-Hate Campaign Through the Use of Social Media Platforms
Bibliographic record
Abstract
Hate has been a growing concern with hate-groups and individuals using the Internet, or more specifically, social media platforms, to globalize hate. Since these social media platforms can connect users around the world, hate-organizations are using these connections as opportunities to recruit candidates and spread their propaganda. Without opposing views, these extreme viewpoints can establish themselves as legitimate and then be used to incite hate in individuals. Thus, these extreme viewpoints must be countered by similar messages to discourage this online hate, and one such way is to use the same platforms through grassroots movements. This paper presents a case study which was conducted on a class of Criminology students who implemented a grassroots community-based campaign called Unity Starts with U (USwithU) to counter-hate in a community by using social media platforms to spread messages of inclusion and share experiences. The results from the campaign showed improvements on people’s attitude towards hate at the local community level. Based on literature and this campaign, policy recommendations are suggested for policymakers to consider when creating or making improvements on counter-narrative programs.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".