Technological Displacement and the Duty to Increase Living Standards: from Left to Right
Bibliographic record
Abstract
Many economists have argued convincingly that automated systems employing present-day artificial intelligence have already caused massive technological displacement, which has led to stagnant real wages, fewer middle- income jobs, and increased economic inequality in developed countries like Canada and the United States. To address this problem various individuals have proposed measures to increase workers’ living standards, including the adoption of a universal basic income, increased public investment in education, increased minimum wages, increased worker control of firms, and investment in a Green New Deal that will provide substantial employment in transitioning to green energy, buildings, and agriculture. In this paper I argue that both left-wing and right-wing positions in political philosophy, such as John Rawls’s Justice as Fairness and Robert Nozick’s Entitlement Theory, are committed to the conclusion that we should take political action to counteract the effects of technological displacement by undertaking such measures to increase workers’ living standards.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".