RECOGNITION OF KAMPUNG TEMATIK ELO PUKEK, PURUS, PADANG THROUGHOUT THE SOCIAL MEDIA \n(Case Study at Kampung Tematik Elo Pukek, Purus, Padang)
Bibliographic record
Abstract
Kampung Tematik Elo Pukek, an area located at the seashore of Padang in West Sumatra, Indonesia, was inaugurated on the 21st of August 2022 by the Minister of Maritime Affairs and Fisheries of the Republic of Indonesia, Sakti Wahyu Trenggono. Kampung Tematik Elo Pukek is known as a fishing area and its unique and traditional way of fishing, the maelo pukek. Promotion of this area in Purus is still lacking even though it received its recognition and also lack of update on their social media made for Kampung Tematik Elo Pukek, Purus. This study aims to find out the ideas on how to promote Kampung Tematik Elo Pukek through social media and what the benefits are. Furthermore, also what the community lacks in support and the needs to develop. Therefore, the methods used to collect the data are through interviews, observation and documentations. As a result, the benefits on receiving more recognition are the positive effects that it can bring on the economic, social and cultural aspects of Kampung Tematik Elo Pukek, which contributes to the overall development and well-being of the community. \n \nKeywords: community empowerment, local participation, Elo Pukek, recognition’s benefit
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".