A Cautionary Discussion about Relying on Human Capital Policy to Meet Redistributive Goals
Bibliographic record
Abstract
Human capital policy has come to be seen as something of a panacea: acting both as a necessary input for modern growth and an effective tool for redistribution. Canada has moved strongly in the direction of linking redistribution and human capital investment. In this paper, I investigate the empirical evidence and theoretical arguments behind the claim that increasing skills investment opportunities and tying transfers to human capital investments while decreasing traditional income support will ultimately lead to a more equal, more just society. Whether this is true, of course, will depend on the notion of justice one adopts. One of my goals in this paper is to examine the implications of the kind of policy path Canada is following under different notions of fairness. I conclude that both empirical evidence and fairness considerations indicate that human capital policy does not make good redistributive policy.
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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 | 0.033 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.020 | 0.040 |
| Insufficient payload (model declined to judge) | 0.009 | 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".