Impacts of the COVID-19 Pandemic on Artisanal Fisheries and Education in The Gambia
Bibliographic record
Abstract
The Gambia is one of the least developed countries in sub-Sahara Africa. In response to the COVID-19 outbreak, the Gambian government implemented a lockdown and various restrictions in 2020, but the impacts on Gambians’ livelihood remained unclear. With the gradual relaxation of the lockdown and restrictions, we were able to conduct the first questionnaire surveys to interview 140 fishermen, 140 fishmongers and 80 customers in the artisanal fisheries sector, and 150 students (grade 9-12) and 14 teachers in the education sector, to assess the impact of the pandemic on their socio-economic and personal well-being. Both fishermen and fishmongers experienced a drop in sales, whereas customers had to pay more during the pandemic. Illegal fishing, lack of policy and regulations and price hikes were among the main concerns for the artisanal fisheries sector, although the respondents did not feel a change in their personal well-being due to the pandemic. Students and teachers did not have the necessary training or resources to conduct remote learning during the pandemic, and both attendance and academic performance declined as a result. Access to the internet and learning materials was very limited, and 10% of the students ceased learning activities altogether. 19% of students and 50% of teachers experienced poor mental health during the pandemic. The majority of the students were concerned about the impact of the pandemic on their education, whereas most teachers were concerned about their finances and psychological conditions. Nearly a quarter of the students relied on unofficial channels to learn about the pandemic, making them susceptible to misinformation. To safeguard Gambian’s well-being against future pandemics or similar large-scale disruptions, we recommend better fishery monitoring and policy enforcement, more fish storage facilities, improving digital learning capacity, providing mental health care in schools, and devising effective communication campaigns about the pandemic.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".