Bibliographic record
Abstract
This study aims to investigate the protest signs that suddenly flooded the streets of Malang, Indonesia, after the death of 135 soccer fans caused by tear gas fired by the police, known as the Kanjuruhan Football Tragedy. This study seeks to answer the following questions: 1. Who or what were the referents of the signs? 2. What languages were used in the signs? 3. What was the function of each language? 4. What were the themes of the protest discourse in each language? 5. What was the narrative rationality of the extracted themes? Ninety-two types of signs comprised the corpus of the study. To systematically interpret the protesters’ messages, we used the descriptive interpretative method to analyze and interpret the data, conducting content analysis, sense-making analysis based on narrative paradigm, and visual analysis to extract the themes of the protests. Then, we constructed a chronological narrative to represent the protesters’ experiences. Almost all of the referents of the signs were for law enforcement officers. Five languages expressed the thirteen themes of the demand for justice and anger. Indonesian was used for all 13 themes, English – 8 themes, Javanese – 2 themes, Boso Walikan, and Arabic - 1 theme each. Unlike the common protest signs, English remarkably captured international attention. English slogans were mostly borrowed from George Floyd’s protests. The harsh protest signs indicated a change in public behavior toward the authorities, especially the police when they demanded justice. Despite the rigid protests in texts and 3D artifacts, the police refrained from actions. Keywords: linguistic landscape, football fan protest, protest signs, Malang, Indonesia. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.62.6
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".