Bibliographic record
Abstract
“More Inequality” can mean either more inequality in comparisons of different societies at a particular point in time or more inequality over time within a given society. Cross-sectional comparisons argue that countries with higher long-run levels of inequality of income are more unhealthy, less democratic, more crime-infested, less happy, less mobile and less equal in economic opportunity – but controversy surrounds some estimates. More importantly, cross-sectional comparisons of the implications of different levels of economic inequality implicitly presume that these represent steady state outcomes. The paper compares long-run levels of real income growth at the very top, and for the bottom 90% and bottom 99% in the U.S., Canada and Australia to illustrate the uniqueness of the post-WWII period of balanced growth (and consequent stability of the income distribution). The new normal of the U.S., Canada and Australia is unbalanced growth – specifically, over the last thirty years the incomes of the top 1% have grown significantly more rapidly than those of everyone else. The paper examines whether there is a plausible auto-equilibrating market mechanism that will equalize income growth rates and stabilize inequality. Unbalanced income growth necessarily implies changes in consumption and savings flows which accumulate to changed stocks of indebtedness, financial fragility and periodic macro-economic crises. Greater economic and sociopolitical instability is therefore a key implication of more inequality over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".