Economic Theory and Practical Lessons for Measuring Equality of Opportunities
Bibliographic record
Abstract
The development of a dashboard of statistics for the monitoring of equality of opportunity should recognize important lessons from economic theory: first, descriptive statistics associated with intergenerational mobility do not speak directly to equality of opportunity without accepting a value judgment that children should not be held responsible for circumstances beyond their control; and, second, the process of child development encourages a focus on different skills and competencies, as well as different stages in a child’s life. On the basis of these lessons, the paper offers three practical recommendations for the development of policy relevant indicators. First, use data appropriate for the country at hand to estimate summary measures of inter-generational mobility, including the intergenerational elasticity of earnings between parents and children, and associated transition matrices. Second, develop measures of absolute mobility, and in particular develop a poverty line based upon the minimal level of resources needed to reasonably lower the risk of intergenerational transmission of low status, and that could complement more traditional poverty lines. Finally, make full use of the information on 15 year-olds from the Programme for International Student Assessment (PISA), and expand its scope to include younger children by developing a PISA type instrument for four to five year old children across the OECD countries.
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 | 0.033 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".