Population vs. Poverty Level in the Future in Indonesia: Holt’s Linear Trend Method
Bibliographic record
Abstract
Poverty is one of the major issues in the field of economics and a serious focus for finding effective solutions.Population size is also a key factor that can influence the poverty rate.This research aims to evaluate the conditions of poverty and the population of Indonesia in the next five years, from 2023 to 2027.Forecasting methods are used, specifically Exponential Smoothing and Holt's method, due to the presence of trends in the data.With the assistance of machine learning technology, particularly using the R, poverty rates and population figures can be projected.The results of the projections indicate that the population of Indonesia is expected to continue increasing each year from 2023 to 2027 or have a positive trend.On the other hand, the poverty rate in Indonesia is projected to decrease each year or have a negative trend during the same period, from 2023 to 2027.This research has important implications for policymakers, as it underscores the significance of data-driven decision-making and informed policy development.The positive demographic trend suggests the necessity of preparing for the associated challenges and requirements of a growing population.Meanwhile, the declining poverty rate presents an opportunity for socio-economic development, necessitating sustained efforts to maintain this trajectory.The study underscores the importance of data analysis and forecasting in addressing critical issues like population growth and poverty reduction, ultimately contributing to the well-being and advancement of Indonesian society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".