Bibliographic record
Abstract
Historically, discussions of income inequality have emphasised cross-sectional comparisons of levels of inequality of income. These comparisons have been used to argue that countries with more inequality are less healthy, less democratic, more crime-infested, less happy, less mobile and less equal in economic opportunity, but such comparisons implicitly presume that current levels of inequality are steady state outcomes. However, the income distribution can only remain stable if the growth rate of income is equal at all percentiles of the distribution. This paper compares long-run levels of real income growth at the very top, and for the bottom 90% and bottom 99% in the United States, Canada and Australia to illustrate the uniqueness of the post-WWII period of balanced growth (and consequent stability in the income distribution). The ‘new normal’ of the United States, 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 asks if auto-equilibrating market mechanisms will spontaneously equalise income growth rates and stabilise inequality. It concludes that the more likely scenario is continued unbalanced income growth. This, in turn, implies, on the economic side, consumption and savings flows which accumulate to changed stocks of indebtedness, financial fragility, and periodic macroeconomic crises; and, on the social side, to increasing inequality of opportunity and political influence. Greater economic and socio-political instabilities are therefore the most likely consequence of increasing income 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".