Is tourism employment a sufficient mechanism for poverty reduction? A case study from Nkhata Bay, Malawi
Bibliographic record
Abstract
Extensive attention has been paid to the magnitude and distribution of economic benefits derived from tourism employment by impoverished populations. However, less is known about how economic benefits, such as increased income, relate to poverty conditions at the intra-household level, particularly within the unique contexts of least developed countries. In this paper, we examine the relationship of tourism employment to poverty conditions in a lakeshore community in Malawi. First, we quantitatively compare employment and poverty conditions among the households of tourism employees with those of employees of non-tourism sectors. Secondly, we undertake a qualitative investigation into lodge employment, and its remuneration and fringe benefits from the perspectives of lodge owners and employees. Our findings of the former analysis reveal that while employees of the tourism sector experienced better working and monetary conditions, this group did not exhibit an improved status in other poverty conditions. The latter qualitative analysis shows that most tourism lodge owners adhered to labour standards of minimum wage, and voluntarily offered fringe benefits such as paying medical, funeral, and education expenses of lodge employees. However, despite this adherence to labour standards, there is little evidence that lodge employees and their households experience an improvement in poverty conditions.
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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 | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".