PERBANDINGAN TINGKAT INFLASI PROVINSI RIAU DENGAN TINGKAT INFLASI PROVINSI YANG BERBATASAN LANGSUNG DENGAN PROVINSU RIAU (SUMATERA UTARA, SUMATERA BARAT DAN JAMBI) SELAMA PERIODE 2009 - 2013
Bibliographic record
Abstract
Inflation is a process of rising prices in general and continuous with regard to market mechanisms induced by different factor,among other things, incrased community consumption, excess liquidity in the market that fueled the consumption or even speculation and the distribution of goods that are not fluently. In other words, inflation is also the currency values serially.Inflation is a process, not an event of high or low price levels. This means that the price level is considered high is not necessarily indicate inflation. The data used in this research is the use of secondary data which come from Bank of Indonesia, BPS, and Finance of Indonesia. The data are obtained the quarterly reports and the annual report. The research results are either quarterly or annually, inflation increases occurred at a quarterly-III every year due to the quarter to concide with Ramadhan and Eidul-Fitri.The inflation hike was mostly experienced by volatile food group. Rising inflation also accompanied the influx of new education school year due to soaring prices for all school levels. And inflation rise on quarter-III this also happens annually in every province, Riau, West Sumatera,North Sumatera dan Jambi. Keywords: Inflation
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".