Perkembangan Makroekonomi Negara Kanada Dengan Analisa Vector Error Correction Model (VECM)
Bibliographic record
Abstract
This study aims to analyze the relationship between Gross Domestic Product (GDP), Inflation (INF), Import (IMP) and Unemployment (UEM) that occurred in Canada by using Vector Error Correction Model (VECM) analysis. The data source comes from https://www.imf.org; data taken from 1980 to 2020. The analytical tool used is the Vector Error Correction Model (VECM) which aims to analyze the relationship or causality between variables both in the short and long term, where the results obtained are the relationship between variables more referring to short term causality. And to find out the impact between variables, this study uses an Impulse Response Function (IRF) analysis where the results that have a positive impact during the Covid-19 pandemic are import shocks to GDP in Canada while the results that have a negative impact are import shocks on unemployment and also Canada's unemployment rate against GDP during the COVID-19 pandemic.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".