Rebirth public creativity and reconciliation conflict towards mural in Solo, Indonesia (iconography-iconology analysis)
Bibliographic record
Abstract
Murals recently gained popularity in Indonesia due to the case of mural bombardment in several regions in 2021 related to the effect of the covid-19 outbreak. There were a lot of murals and graffiti that criticized the President and the government in Jakarta and Surabaya. However, murals in Solo, especially on Gatot Subroto (Gatsu) street, tend to illustrate the picture of happiness rather than critics to others. Hence, people believe these murals have an impact on tourism in Solo. This case makes murals suddenly become popular, and as a consequence, people are curious about the function of the murals. This research focuses on the meaning of murals in Solo, which is different from other cities in Indonesia by using a qualitative methodology. For collecting the data, we conducted interviews, observation, documentation, and literature. Then, for data analysis, we used the Iconography-Iconology method from Erwin Panofsky which is divided into three steps such as pre-iconography, iconography analysis, and iconology interpretation. This research provides three meanings of Gatsu’s murals that consist of the styles, theme, and intrinsic meaning: (1) the actual meaning is that Gatsu’s murals use the eclectic style; (2) the conceptual meaning is a peaceful and positive theme which is caused by the reason that Gatsu used to be the center of the artwork; (3) the intrinsic meaning is to reborn creativity which tends to reactivate the creative mood in the city. In addition, it is an endeavor to resolve the dispute in Solo, particularly at Gatsu street, which has been traumatized by several conflicts (the G30SPKI incident, the central java riot, the revolution, and the national awakening party) that happened in the past.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".