Bibliographic record
Abstract
We establish the relationship between economic growth and employment in Uganda (2006–2011). We obtained data from World Development Indicators, Uganda National Household Panel Survey (2011) and United Nations Statistical Data Base and we adopted the Job Generation and Decomposition (JoGGs) Tool of the World Bank for the analysis. The growth profile for the period 2006–2011 was jobless as evidenced by 36% change in per capita GDP emerging from a decrease in the employment rate. Agricultural sector registered the greatest dampening effect on overall value added per person and to the share of the employed in the population of working age by 31% and 6.5%, respectively. Manufacturing sector contributed positively to the change in per capita GDP by 8% but negatively to change in total employment rate by 0.2%. Positive contributions to the employment rate and per capita GDP were observed in the services and industrial sectors. It is further noted that productivity or output per worker contributed over 100% to the overall growth in value added per person. In terms of labor productivity, the lowest was in the agricultural sector and the highest was in the industry followed by the services sector. The inter-sectoral shifts positively contributed to labor productivity which implies that there was a relocation of labor from less efficient to more efficient sectors. The demographic transition is a promising source of increase in per capita income; the dependence ratio has reduced and this has clear dampening effect on poverty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".