Bibliographic record
Abstract
Quality is one of the important activities as promotion National Assessment and Accreditation Council (NAAC) with in overall mandate. Progressively Higher Education Institutions (HEIs) are coming forward for assessment and accreditation, there is a need for more trained personnel in quality assurance. In order to generate the concept of quality assurance in higher education and arrange ‘assessors’ to consume the duty of third party assessment and internal quality assurance, NAAC is in the process of developing a series of publications on quality in higher education. This module entitled “Quality Assurance in Higher Education: An Introduction” developed in collaboration with the Commonwealth of Learning, Vancouver, Canada is first in the series that point at providing a ground perception of quality in wide and its appeal to higher education in particular. This module will elucidate copious terms, models and practices sweepingly nearly new in the factors of standard assurance in higher education. Present paper appear the quality assurance in NAAC and how it works
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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 | 0.050 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".