Creating Curriculum of English for Conservative Tourism for Junior Guides to Promote Tourist Attractions in Thailand
Bibliographic record
Abstract
This research was supported the research fund of 2017 by Office of the Higher Education Commission of Thailand. The objectives of this research are listed below.1). To form the model of teaching and learning English for local development by English curriculum (B. Ed.) students’ participation in training on out-of-classroom learning management, which focuses on the students’ English skills improvement along with developing the sense of love of their home towns.2). To create curriculum of English training for conservative tourism for junior guides in Sung Noen District, Nakhon Ratchasima Province.3). To promote conservative tourist attractions in Sung Noen District, Nakhon Ratchasima Province among foreign tourists, and to boost the local economy so that young generations can earn income and rely on themselves in the future.An interesting result from the research was more income gained from tourism in Sung Noen District, Nakhon Ratchasima Province between April 2016 and June in the same year. The junior guides’ ability to communicate and provide information about tourism in English was evaluated. This result also accorded with the evaluation done by the youth and stakeholders on the curriculum of English for conservative tourism for junior guides, and 75 percent considered it very good, matching with the synthesis from the interview. The curriculum was created to be applicable to the local tourism context and match the need of users.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".