Development of the SiAlong Platform to Improve Digital Literacy on Landslide Disasters among Generation Z in Semarang City
Bibliographic record
Abstract
This study is a development research which proposes to create a digital platform to enhance digital literacy concerning landslide disasters for Generation Z.The findings indicate that in Semarang City, Generation Z frequently accesses information from the internet and finds it extremely helpful.However, their knowledge and literacy regarding preparedness for landslide disasters remain low, which highlights their need for a digital literacy platform on this topic.Based on this requirement, the researchers developed the SiAlong platform, short for "Landslide Preparedness Information System".This platform is a website accessible by users on various devices, including Android and iOS smartphones, as well as computers, laptops, PCs, and tablets.To date, the availability of digital platforms for information and education on landslide disasters is very limited.Digital technology is predominantly used for developing sensing systems, detection, prediction, or monitoring of landslide disasters.The SiAlong platform offers a range of features designed to improve disaster digital literacy, including: (1) Landslide Concepts, (2) Landslide Risk Reduction, (3) Safety Tips, (4) Do's and Don'ts, (5) Emergency Contact, (6) Survival Kit Checklist, (7) Video Content.Additionally, SiAlong includes a WEBGIS-based Landslide Disaster Risk Map feature that allows users to recognize and assess the level of danger and risk of landslides in their living areas.Thus, SiAlong presents integrated content on landslide disaster literacy, enhanced with a WEBGIS feature.The development of the SiAlong platform is expected to realize effective digitalbased disaster education, thereby increasing the knowledge, awareness, literacy, and skills related to disasters among Generation Z.The widespread use of the SiAlong platform will enhance its utility.Besides dissemination, improving the quality and interactive features of the SiAlong platform is also essential.The SiAlong platform must not remain static but should continuously evolve its features to enhance effectiveness and utility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".