Survey dataset for the perceived consciousness towards environmental sustainability by undergraduate students
Bibliographic record
Abstract
The scope of the dataset allows to examine undergraduate hospitality and tourism students' intentions towards environmental sustainability. Moreover, it is possible to compare factors (knowledge, attitude, perceived behavioral control, and intention) towards environmental sustainability between hospitality and tourism students based on different socio-demographic characteristics. To fulfill these objectives, data was collected through a bilingual questionnaire containing 312 valid responses from undergraduate students studying towards a business degree. The questionnaire was administered at the Prince of Songkla University in Phuket, Thailand in the fourth quarter of 2021. Moreover, the data collection adhered to the NESH principles applicable for Social Science Research. The data collection instrument was validated for international consistency and reliability through a three-person panel of research experts and validated using the IOC method. Furthermore, the questionnaire was tested with a targeted sample consisting of ten students prior to its implementation. The dataset serves as an insightful reference for practitioners and policymakers in higher education to adjust their pedagogy, in addition to, as a secondary data source for educational researchers to examine undergraduate hospitality and tourism students' intentions towards environmental sustainability.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".