The Impact of National Higher Education Sports Event Hosting on Perceptions of Economic Impact in Muallim District, Malaysia
Bibliographic record
Abstract
Introduction: As a way to promote active and healthy lifestyle, many sports events were organized in Malaysia. However, despite many sporting events organized, it is hard to found studies conducted looking into the impact of the national higher education sport event hosting to the perceptions of economic impact. It is important to look into this area, as without any data, we will be blind of whether the hosting will give positive or negative impacts, or if rather it is positive, is there any other ways for it to be improved. This study aim to find out the impact of a national sports event hosting namely Sukan Institusi Pengajian Tinggi (SUKIPT) or Higher Education Sports on economic sectors in Muallim District, Malaysia. Methods: Questionnaires were used to gain the data among Muallim residents. 150 peoples that involved in foods/restaurant, accommodations, food delivery services, transportation and retails were recruited as respondents. They were given questionnaires on their perception of the economic impacts on their businesses. Descriptive statistics were used to gain mean and standard deviation while Analysis of Variances were used to compare the economic impacts between sectors. Results and discussion: Results showed that this event has increased the product and services demand that then contributed to more sales especially in food/restaurant sectors. We also found the transportation sectors to be the least benefit sectors that should be related to easiness or difficulties for them to get the public transport. Conclusions: The findings of this study can be referred by the authorities especially government generally, and municipality and universities specifically for the planning of next events to be organized in order to enhance socioeconomic values and promote active and healthy lifestyle in all parts in the country.
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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 | 0.001 | 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) | 0.005 | 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".