The prospects of tourism and hospitality industries as drivers of Local Economic Development (LED): The case of Port St Johns (PSJ), Eastern Cape, South Africa
Bibliographic record
Abstract
Globally, the tourism and hospitality sectors contribute meaningfully to both developing and developed economies. These sectors have been identified as drivers of local economies due to the potential number of jobs they can create. However, Port St Johns (PSJ) remains one of the poorest tourism regions despite the number of tourists that come to the area and the revenue generated through these sectors. Consequently, the paper explores the prospects of tourism and hospitality contribution to local economic development in the context of PSJ. A simple random sampling technique, characterised by face-to-face surveys on the residents in PSJ was utilised to collect data. The findings indicate that the majority (75%) of respondents are aware of tourism development activities that take place in PSJ and the potential to contribute to Local Economic Development (LED). The findings of this paper recommend that PSJ tourism stakeholders (public sector, private sector and local communities) should partner to ensure that tourism development initiatives that take place in the area are optimised. These findings have implications for the stakeholders such as local business, tourism planners, community and the municipality that are responsible to manage the local industry. Furthermore, stakeholders must be part of the development process from the outset. Hence it is recommended that the findings of this paper be utilised as a basis of developing an opposite strategy for tourism and hospitality industries to drive LED.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".