Factors Affecting Unemployment Rate in Canada and Denmark
Bibliographic record
Abstract
This study investigates the impact of labor force, government expenditure, gross capital formation, foreign direct investment, energy and domestic credit to private sector on unemployment in Canada and Denmark. Annual data is collected from 1980 to 2021 from world development indicator. Johansen Cointegration approach is used to identify the relationship between dependent and independent variables. The results show that labor force and government expenditure positively and significantly impact unemployment in long run in the case of Canada. While the gross capital formation, energy and domestic credit to private sector have negative but significant impact on unemployment. In short run labor force, GCF, energy and domestic credit to private sector has positive and significant impact on unemployment. On the other hand, in the case of Denmark, labor force and government expenditure have positive and significant impact on unemployment. Whereas gross capital formation, energy, FDI and domestic credit to private sector have significant and negative impact on unemployment while in short run FDI shows negative and significant impact on unemployment.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".