Investigation of Labor Market Performances of G7 Countries with Integrated Standard Deviation and Gray Relational Analysis Methods
Bibliographic record
Abstract
The objective of this study is to assess the labor market performance of G7 countries between 2018-2022. For this purpose, Standard Deviation (SD) and Gray Relational Analysis (GRA) methods from Multi-Criteria Decision Making methods were used in an integrated manner to evaluate the labor market performance of the relevant countries in the relevant years. First of all, criterion weights of 8 criteria, which are considered to affect the labor market performance, were obtained by SD method. Based on the results of this method, the criterion that has the greatest influence on labor market performance is the temporary employment criterion in all years except 2020 and 2021. Then, the labor market performance of G7 countries in the years of interest was evaluated with the GRA method. In conclusion, the three countries with the highest labor market performance were Canada, France and the United Kingdom in 2018-2020, and France, Canada and the United Kingdom in 2021 and 2022. Italy ranks last in all years.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".