Langkah Kecil-Dampak Besar: Walking Tour sebagai Kunci Pariwisata Berkelanjutan di Kampung Peneleh Surabaya
Bibliographic record
Abstract
Title: Small Steps-Big Impact: Walking Tour as Key to Sustainable Tourism in Kampung Peneleh Surabaya A way to preserve historical areas through tourism is by organizing walking tours. Recently, there has been a surge in walking tours offering experiences in spatial and architectural history for both domestic and international tourists. One example is the locally organized walking tours. However, studies focusing on tourist experiences during these walking tours, directly involving tourists as participants, are still limited. Identifying observation points along the walking tour route is crucial to determine which sites elicit positive or negative responses from participants. This article aims to explore tourists' spatial and architectural experiences and assess the contribution of walking tours to sustainable tourism. The research focuses on a case study in the historical village of Peneleh, Surabaya, where walking tours are conducted by a tourism awareness group (pokdarwis). The study employs a qualitative approach, utilizing observation and photo surveys, with six tourists serving as participants who joined other tourists on a walking tour package. The results show 154 photos representing four participant responses: liked photos (50.6%), disliked (10.4%), helpful during the tour (19.5%), and areas needing improvement (19.5%). Participants captured liked objects while also noting areas that require improvement. Additionally, walking tours offer broader access to various tourist sites that are not always open to the public. Those initiated by Pokdarwis can be sustainable tourism activities, continually innovating tour themes and offering new experiences for tourists.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".