Mengukur Output Gap Ekonomi Maluku Utara (Pendekatan Hodrick-Prescott Filter)
Bibliographic record
Abstract
Development anywhere will always be faced with fluctuating conditions or the ups and downs of economic growth. In the discourse "New Neo-Classical Synthesis" which by (Hubbard, 2014; Gordon, 2014; Mitchell, 2019 and Insukindro, 2020), sees economic fluctuations that can lead to recession and expansion. How to measure the output gap or economic fluctuation that occurred in North Maluku Province and its relationship with economic indicators. Estimation of economic fluctuations uses the Hodrick-Prescott Filter method, which is an econometric method to describe the frequency of time series data becoming trends in an economic cycle. The results showed that North Maluku's economic fluctuations were actually dominated by economic recessions rather than expansion. In the first quarter of 2020, if you use the standard macroeconomic approach, there will still be economic expansion fluctuations (3.06), but the New Neo-Classical Synthesis method since the first quarter has experienced an economic recession (-1.42). Meanwhile, the movement patterns are unidirectional or procyclic, there are also opposites or contersiclic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".