Accessibility of higher education: socio-economic aspect
Bibliographic record
Abstract
The paper is devoted to investigation of the problems of inequality in higher education in three main aspects:• Analysis of the results of the global rankings of national educational systems on access to higher education;• The role of mass distance education in promoting equality in higher education;• International university rankings in the context of the development of elite and mass higher education.1. Analysis of the results of two global rankings of national educational systems (by the Canadian researchers Alex Usher, Amy Cervenan, Jon Medow), confirmed the expediency of the two rankings that divide the social and economic aspects of inequality in higher education. To assess Russia's place among 14 countries surveyed, there were determined the approximate values of the indicators of both rankings characterizing the Russian system of higher education: the values of eight indicators for determining affordability, and four indicators for the social aspects of accessibility. When determining the strengths and weaknesses of the Russian higher education system the results of the rankings for each indicator were taken into account.The main results of the assessment of inequality in Russian higher education:• At an estimated assessment Russia refers to one of the least successful countries with the low ranking of the accessibility of higher education (social aspect) and with a relatively good indicator in the ranking of affordability of higher education (financial aspect);• In the final ranking of accessibility (social aspect) Russia occupies one of the last places due to the low rate of social equality in education, which has the maximum weight of the four indicators. The values of Russian indices of three other indicators are relatively high: 3-4th place on the participation of young people in higher education; 4-5th place on the achieved level in higher education of young people; 8th place in gender parity index.2. The case of Russia shows the efficiency of mass distance-teaching University (mega-University) raising equity in access to higher education, including people living in geographically remote areas and socially vulnerable groups of the population (persons with disabilities, prisoners, military personnel etc.). 3. Consideration of methodological bases and the results of five international university rankings (2007-2013) showed:• The role of Webometrics Ranking of World Universities in the assessment of national systems of higher education, taking into account the scale factor and the development of regional universities in the country.• The growth of the network activity of Russian universities, especially regional (in the latest rankings of Webometrics among 3000 world's best universities are universities of all federal districts of Russia).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".