ETHICS AND SUSTAINABILITY: ELICITING MILLENNIAL PERSPECTIVES ON THE ETHICS WITHIN PHARMACEUTICALS
Bibliographic record
Abstract
Americans spend $392 billion in prescribed medication per Washington post; Wall Street Journal study compared branded rugs in Ontario, Canada, England, Norway the findings confirmed that United States prices were higher prices than Norway; and England. United States with the highest prices contribute to price gouging? EpiPen by Mylan from $2 overseas and $750; Daraprim by Turning Pharmaceuticals gouge from $13.50 to $750 per pill; and Cosmegen $20 to $30 overseas and $1400 per injection these prices are horrendous especially when the drugs are required for debilitating diseases. Ebola scare in 2014 required protective devices against contamination of the virus. Kimberly Clark and Halyard Health manufactured, marketed, and sold Microcool gowns with a 77% failure rate as confirmed by Intertek Labs. What contributes to the pharmaceutical unethical conduct that includes price gouging and exploitation? Pharmaceuticals growing with an emphasis on buying rights to drugs and drastically increasing the cost of drugs and medical devices without substitutes ethical; what do millennials who are projected by Fortune to dominate the workforce by 2020 think on ethics within pharmaceuticals? The findings confirmed that the pharmaceuticals price gouging, sale of drugs and products that are later recalled, and huge budgets in marketing instead of R&D weren’t ethical and results confirmed that the pharmaceutical companies should focus on their core which is saving patients’ lives. JEL: L60, L65, I18 A14, L29, K32, M14 Article visualizations:
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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 | 0.050 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".