The Minimum Wage as Method of Alleviating Poverty: Current Practices versus Alternative Policy and Legal Resolutions
Bibliographic record
Abstract
Poverty is a social issue that impacts much of the world, including the United States. Oftentimes, proponents of the minimum wage argue that a higher minimum wage would help alleviate poverty in the country. Whether or not there will be impacts, or how significant the impact will be, is a subject of debate. The paper first analyzes arguments in support of using the minimum wage to reduce poverty in the US. Then, arguments against the current minimum wage are presented. Discussion regarding alternatives or alterations to the current minimum wage is raised at the end of the paper that would provide a more sustainable and legally sound public policy choice. This includes an analysis of the current minimum wage policies in the city of Philadelphia as example.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".