Constructing an Enhanced Unemployment Model in Canada, Using ARDL
Bibliographic record
Abstract
The unemployment rate in Canada has been increasing from 2019 to 2023 and this high volatility of Canada’s unemployment rate has drawn researchers’ attention in recent times. Quarterly data from OECD Data, World Bank, IMF Data and DataStream, were collected from 1993 to 2023, to examine the factors affecting unemployment rate in Canada, using the Unit Root Test and the Autoregressive Distributed Lag (ARDL) approach. ARDL was used to study the long run effect between unemployment rate and related factors. This approach can be used only when Unit Root Test had been applied. The results of the study revealed that only Gross Domestic Product (GDP) reported a short run causality with the unemployment rate in Canada while other independent variables did not. The study indicated no long run causality relationship between all independent variables, namely population, GDP, inflation, foreign direct investment and average wages, as the F-statistics was below the threshold value. This study added a new independent variable, that is average wages, in the study of unemployment rate in Canada. This study covered the period from 1993 until 2023.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".