L’efficacité, l’égalité, l’équité et la répartition personnelle des revenus
Bibliographic record
Abstract
The analysis of the impact of economic policy and of the process of growth on personal income distribution is getting greater priority in the literature. Such an analysis cannot usefully be pursued in the light of efficiency considerations alone; it must also take equality and equity into account. Equity is defined here as a characterization of a state of affairs in terms of three parameters: (1) the choice of a distributive norm, (2) the specification of what is to be distributed, and (3) a measure of the degree of inequality that exists. A general "equity function" (E) is defined, whose particular form reflects the chosen distributive norm, and whose two arguments are the sum total to be distributed and a measure of the inequality that characterizes the distribution of that sum. Then, a number of critera of distributive justice are compared, and the criteria based on the relative and absolute income gaps are found to be the most useful. This suggests the formulation of an "equity index" (e) which is sensitive to both growth, relative inequality and absolute inequality. Unsurprisingly, empirical estimates show that the "equity index" has risen in socialist countries and fallen in non-socialist countries as a group. The "equity index" is also estimated for a number of individual countries, but the results are difficult to interpret without an in depth analysis of the circumstances of each country.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".