Issues and problems in Higher Education from the perspective of India and Canada
Bibliographic record
Abstract
Globalisation is a phenomenon that is transforming the world economic system including nearly all aspects of production, distribution and other business processes.Globalisation has changed scenario of Education too.Every nation has its specific thrust areas for the development of nation depending upon the requirement of the country.Education is one of the thrust areas for the development of the country economically, technologically and politically.Education system of any nation bridges the gap between the people of different community, Caste, Gender etc. Countries have their own provision in terms of policies and practices at university level.In India there is a National policy of Education (NPE, 1986) and Program of Action (POA 1992) and five year developmental plan along with National assessment and Accreditation Council to maintain quality in higher education and at the same time to bring measures for equity and equality in higher education.Similarly in a developed country like Canada, there is Council of Ministers of Education Canada (CMEC) actively engaged for minimizing gap between social barriers like gender, culture etc. Though there is vast difference between countries like Canada and India in terms of Geographical area, Population, Language, literacy rate etc. it would be interesting to study the issues and problems faced by higher education and teacher education in particular of both the countries.In this paper authors have discussed comparative scenario of higher education and in particular teacher education in Canada and India in terms of quality input.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".