Qualified Equal Opportunity and Conditional Mobility: Gender Equity and Educational Attainment in Canada
Bibliographic record
Abstract
“Equalizing opportunities”and “leveling playing fields”, policy objectives aiming to make outcomes independent of circumstance, underly the interest in generational mobility. However there is little empirical evidence that such policies have ever been effective in terms of that aim. Here it is shown that, absent any other policy objective, pure equal opportunity policies are symmetric in effect, that is while increasing upward mobility of those poor in circumstance they have the unfortunate concomitant of increasing the downward mobility of those rich in circumstance. The addition of a pseudo “Paretian-Utilitarian”policy imperative (That the inheriting generation should not be made worse off in a first order stochastic dominance sense) yields a “Qualified Equal Opportunity”or “Conditional Mobility”policy. Such policies are asymmetric in effect focusing on improving the mobility of the poorly endowed without diminishing the opportunities of the richly endowed. Their empirical assessment requires that generational mobility relationships be viewed in a different light since post policy outcomes will not in general be completely independent of circumstance and should not be evaluated against that metric. In terms of generational regression models they
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".