Bibliographic record
Abstract
This paper analyzes the use of education spending as a redistributive tool. Individuals generally difier in their innate talents as well as the location they attend school, both of which afiect the accumulation of human capital. If governments can target education funds to speciflc neighbourhoods/regions (i.e. low productivity ones), should they do so? The results suggest that educational transfers are inferior to money transfers as a tool to equalize utilities in a standard consumption/leisure framework. This implies that policies designed to equalize productivities must be justifled on difierent grounds, for example the importance of self-esteem. Further, we show that even if \equalizing opportunities is deemed optimal in the static problem, it may not be a reasonable policy goal when we extend the analysis to include dynamics. This is true because individuals are heterogeneous and because they receive beneflts from living in rich areas. Once educated and earning, people segregate according to income and policy-makers face the same problem next period. Previous education spending decisions in∞uence current human capital accumulation and an equal opportunity policy is seen to be too extreme, as it leads to lower outcomes in the next generation for all socio-economic groups.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".