Bibliographic record
Abstract
Regardless of their level of developments, the income distribution problem is one of the most important economic and social problems the countries face. In recent years, scholars have performed multiple studies to determine the factors affecting income distribution. The purpose of this chapter is to examine the impact of military expenditures on income inequality in a sample of North American countries (the USA, Canada, and Mexico), within the context of the Kuznets curve. The study covers between 1995-2013. In unit root Peseran approach, in cointegration analysis, Durbin-Hausmann approach were employed. The findings show that the coefficient of the military expenditures series is positive and the coefficient of square of the military expenditures is negative. This situation shows that military expenditures first increase and then reduce income inequality. Findings indicate that there is an inverse “U” relationship between military expenditures and income inequality. Moreover, it has been detected that as economic growth increases income inequality decreases.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".