ANALISIS PARAMETER SOSIO-DEMOGRAFIK PROVINSI NUSA TENGGARA BARAT
Bibliographic record
Abstract
Demography has two main aspects, i.e. quantity and quality aspects. The two aspects may be studied through their trends, distributions, growth, and compositions. The demographic process, such as fertility, mortality, and migration, on the other hand, are the factors affecting the dynamics of the demographic aspects. This study aimed at analyzing the socio-demographic parameter or the demographic dynamics of NTB according to the data from the Population Census of 2010. This study specifically analyzed secondary data in which the main data came from the series of result of the Population Census conducted by the Central Bureau of Statistics. The data analysis was carried out using descriptive method by utilizing the statistical tables and graphs to find the trend or the development of the parameter taken as the analytical object. According to the result of the analysis, it could be concluded that: a) the Population Growth Rate of NTB had sharply decreased during the last 3 decades, even to the extent of becoming the fifth lowest in Indonesia during the decade of 2000-2010. However, the distribution was uneven in which most of the population (70%) were concentrated in Lombok Island, whose size is only a quarter of the whole area of NTB; b) the gender ratio of NTB population was the lowest in Indonesia, i.e. 94.26 compared to the national average which reached 101.37; c) the Total Fertility Rate (TFR) dropped from 7.0 in 1971 to 2.4 in 2010 and the most drastic decrease was recorded during the decade of 1990-2000 in which the TFR dropped as much as 61.3% from 5.0 to 3.0. Likewise, the Infant Mortality Rate dropped from 221 per 1000 live births in 1971 to 48 in 2010; d) the quality of NTB population reflected by the Human Development Index (IPM, Indeks Pembangunan Manusia) was very low which was the second lowest after Papua Province. Besides, there was a gap of IPM among the municipalities/regencies in which Mataram City and Bima City had the IPM far above the provincial rate while the other regencies had far below the provincial rate of IPM. Keywords: Population, quantity, quality
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".