Bibliographic record
Abstract
Numerous studies have been conducted on the interpretation and translation of English terms into other languages. The purpose of this study was to identify the adequate Indonesian equivalent terminology for hotel amenities, services, and facilities applied in English and the strategies utilized by both domestic and international hotel guests in understanding the equivalent terms in their native language. Qualitative research methodology was used. The subjects included 10 domestic guests from a 5-star hotel, 10 domestic guests from a 4-star hotel, 5 international guests from a 3-star hotel, and 2 hotel staff from a 5-star hotel, 3 staff from a 4-star hotel, and 1 staff from a 3-star hotel. The findings demonstrated that some of the English terms commonly used in hotels had Indonesian equivalents, and some did not. The international guests strategies were: 1) searching in an online dictionary or a Google search; 2) asking people they met nearby immediately; and 3) guessing the meaning. Domestic guests’ strategies included: (a) asking other guests or hotel staff for clarification; and (b) guessing the meaning. Future research should overcome the limitations of this study, considering translations and linguistic norms training strategies.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".