Bibliographic record
Abstract
Students Income Contingent Loans in OECD Countries. Investment in higher education is important for economic growth. Now, although most of European countries (including France) invest about 1 % of GDP in Higher Education, United State (and Canada) devote to it about 2.5 % of GDP (OCDE [2004]). We also point out that Higher Education spending tend to be more important in countries where a substantial part of the funding is private, originating from students and their families or from donations (alumnies) or enterprises. The difficulties in Higher Education funding that arose in some developed countries during the 90 s, combined with a persistent inequality of opportunities, have conducted some of them to implement reforms. Those reforms have in common some core characteristics that lean on Income Contingent Loans in compensation of the introduction of higher tuition fees. This note presents in details some of theses reforms introduced in United Kingdom, Australia and New Zealand. We also present the case of Sweden, where students loan schemes exist since long although access to Higher Education is totally free in this country.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".