Decomposition of Economic Growth in Sri Lanka: Deep Look into the Service Sector
Bibliographic record
Abstract
The service sector gives the highest contribution to the economic growth of the country and it is about more than 50. Therefore service sector give the highest contribution for the economic growth in Srilanka. Through this research the service sector is decomposed. This empirical study was to measuring the contribution for the economic growth in Sri Lanka by service sector. Time series data is used to identify the decomposition of economic growth in Sri Lanka by Service. Annually data is collected from 2006 to 2014. This study mainly focused on growth decomposition methodology developed by Ivanov and Webster and this methodology used to decompose economic growth in Sri Lanka by service sector. This model presents an approach that is general and it can be applied to other countries. The methodology identifies the direct impacts of specific service sector components on the per capita growth of real gross domestic product. The study found that each service sector components in this analysis has a very different contribution to the growth rate in the economy. The research findings would provide guidance to the policy makers to develop policies, procedures, programs and standards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".