A Storytelling-Based Marketing Strategy Using the Sigale-Gale Storynomics as a Communication Tool for Promoting Toba Tourism
Bibliographic record
Abstract
Lake Toba is one of the super priority tourism destinations declared by the Indonesian government in 2021, intended to revitalize Indonesian tourism that was hit hard by the Covid-19 pandemic.However, a different strategy to promote Lake Toba and other tourism destinations in Indonesia is required, not only depending on natural beauty, but also by providing additional value, particularly by making use of local culture for marketing purposes.This study intends to analyze the utilization of the Sigale-gale storynomics based on storytelling for promoting Toba tourism.This study uses the qualitative approach, with the phenomenology method, because the Sigale-gale storynomics is tightly related to the stories and experiences of the surrounding public, particularly in understanding the meaning of Sigale-gale.The tale of Sigale-gale is closely entwined to Batak culture, especially regarding patriarchal family values and also the importance of the eldest son in the family.This is especially interesting to introduce this cultural value to tourists visiting Lake Toba.The findings from this study show that the use of the Sigale-gale storynomic through storytelling is interesting and can encourage Indonesian tourism.Storynomic packaging can create interesting story content, especially by paying attention to several important elements, namely: conflict, character, plot and message.Interesting storynomic story content supported by delivery through storytelling will be a different tourism marketing communication tool that is expected to encourage growth in the number of visiting tourists.However, the Sigale-gale tale needs also to be standardized into a single version, as there are various versions, with corresponding varied perceptions, among the Toba people.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".