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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it