A Comparative Study of Death Taxes in Thailand, the United Kingdom, Canada, New Zealand, and the United States
Bibliographic record
Abstract
Poverty is an enduring feature of modern human civilization, and the world continues to attempt to address the problem by creating egalitarian laws with the aim of fairer wealth distribution. Many blame the rich as the root cause of poverty - particularly the accumulation of vast sums of wealth and subsequent conveyance of wealth to the next generation. For example, the accumulation of wealth serves to exacerbate wealth disparities by granting the offspring of the rich access to superior education at a young age. A Guardian report showed that the richest one percent of individuals control half of the world's wealth. Death Taxes (also known as Wealth Taxes) are an important step in addressing this historical problem. The death tax is not a new financial tool: the ancient Egyptians made vigorous use of it as early as 700 B.C. More recently, the death tax has been introduced in several countries with an eye towards eliminating unfair distribution by limiting the amount of wealth that can be passed on to the next generation. Despite these efforts, wealth inequality continues to worsen. According to the Organisation for Economic Co-operation and Development, the gap between rich and poor has widened over the past two decades. This paper will examine and explain why Death Taxes are a potential solution to resolving vast wealth inequality.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".