Social Inequalities – Youth Opportunities
Bibliographic record
Abstract
One of the most oft-debated subjects today is the structural changes within education. There are passionate debates about whether young people's inequalities of opportunity have increased or decreased. One view is that the system has become more open during the restructuring, while others argue that the changes that took place in the 1990s led to a more “elite-centric ” system. One group emphasizes the diversity and multifaceted nature of the system, while another points to a deep division within, namely, the growing exclusion of the children of uneducated rural social groups from the system of education. Adherents of one view see the inevitability of the creation of a market-compatible system of schooling, while others point out the destructive effects of the market on equality of opportunity. Those arguing for a market-compatible education—especially in the field of higher education—emphasize that different social groups make conscious choices as to where and in what direction they want to take their careers. The other side argues, however, that social selection was never as strong in Hungary as in the 1990s. This claim is substantiated with data that show that 80 % of students in elite institutions of higher education (arts and sciences, medical school) come from about one quarter of all high schools. The Two Scenarios and Social Inequalities
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".