Energy Transition and its Impact on Employment in East Africa
Bibliographic record
Abstract
Purpose: The main objective of this study was to explore the energy transition and its impact on employment in East Africa. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings revealed that there exists a contextual and methodological gap relating to energy transition and its impact on employment in East Africa. Preliminary empirical review revealed that the energy transition presents a significant opportunity for East Africa to address its energy needs, combat climate change, and create employment opportunities for its growing population. However, realizing the full potential of this transition requires a coordinated effort from governments, businesses, and civil society. Policies and investments should be designed to ensure that the benefits are inclusive, reaching all segments of society, including women and marginalized communities. By addressing the challenges and leveraging the opportunities of the energy transition, East Africa can not only achieve its energy and environmental goals but also contribute to sustainable economic development and improved livelihoods for its people. This study serves as a foundation for further research and policy action in this critical area, emphasizing the need for a holistic and equitable approach to the energy transition in East Africa. Unique Contribution to Theory, Practice and Policy: The Human Capital theory, Structural Transformation theory and the Just Transition theory may be used to anchor future studies on energy transition and employment. The study made the following recommendations: investing in workforce development and training programs, promoting gender inclusive employment, facilitating just transition mechanisms, supporting community based renewable energy projects and fostering public-private partnerships.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".