Livelihood access and challenges of coastal communities: insights from Ghana
Bibliographic record
Abstract
Purpose The purpose of this study is to contribute to the discourse on livelihood access and challenges of fisherfolks and farmers within coastal communities in Ghana. Insights from such studies can help to create win-win outcomes between communities and oil companies and give the latter social license to operate. Also, it will help to identify the existing knowledge gaps that still need to be filled and contribute to the overall management of Ghana’s oil resources. It will further contribute to the government’s livelihood diversification programs in oil-producing communities. Design/methodology/approach The study employed the use of qualitative research paradigm to collect primary data in oil- and gas-producing communities in the Western Region of Ghana. Specifically, focus group discussions and in-depth interviews were conducted among diverse stakeholders. Findings Findings from the study show that several people and households along the coast of Ghana’s Western Region depend on the fishing industry as their livelihoods. However, fisherfolks are facing several challenges due to oil production. For instance, the quantity of fish harvest has reduced drastically since oil production started in 2010. Farming activities have also been adversely affected. The study has unearthed that the existing social and economic infrastructure are very limited to support the development of the coastal communities in Ghana’s Western Region. The study suggests that to deal with some of the challenges faced by coastal communities, livelihood diversification programs should be introduced. Research limitations/implications Not every community within the oil and gas areas in the Western Region was covered. Future work will address this limitation. Practical implications The study has revealed that the Metropolitan, Municipal and District Assemblies need to expedite the process of conducting a comprehensive needs assessment of communities and capture them in their medium-term development plans. Social implications The corporate social responsibility programs will create win-win outcomes between oil companies and communities. Originality/value The study is an original piece of work with data collected from the field. The study will contribute to the efficient management of natural resources in Ghana and other developing countries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".