DIGITAL STORYTELLING SEBAGAI METODE CAPTURE PENGETAHUAN ADAT MINANG: PELUANG DAN TANTANGAN DI ERA 5.0
Bibliographic record
Abstract
The digital transformation in the era of Society 5.0 encourages the integration of technology with cultural preservation, including efforts to sustain Minangkabau indigenous knowledge traditionally passed down orally. Local wisdom such as pepatah-petitih (proverbs), kaba (epic tales), and pantun (poetic expressions) represents forms of tacit knowledge that are at risk of disappearing due to generational gaps and the lack of systematic documentation. This article explores two key questions: how can digital storytelling (DST) function as a knowledge capture method for Minangkabau traditional knowledge, and what are the opportunities and challenges involved in this process? This study uses a qualitative approach, including literature review, reflective analysis, and case studies of cultural narrative digitization initiatives within local communities and academic libraries. Findings indicate that DST is effective in transforming oral knowledge into digital formats that are both communicative and participatory, especially through the involvement of youth, librarians, and traditional leaders. Major opportunities include community collaboration and the use of digital platforms such as YouTube, institutional repositories, and social media. However, challenges include technological limitations, metadata standardization, and cultural sensitivity in documentation. This study concludes that digital storytelling can serve as a bridge between technology and tradition, provided that it is supported by information literacy policies, community training, and multi-stakeholder collaboration. The model offers strong potential to sustain Minangkabau indigenous knowledge in an increasingly digital and global context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".