Issues in Higher Education: Analysis of 2017 Global Knowledge Index Data and Lessons Learned
Bibliographic record
Abstract
Despite considerable efforts to increase the quality of Higher Education (HE) in many countries, the absence of a methodology to guide scholars and policymakers to assess its quality has been a barrier. In 2017, the United Nations Development Program (UNDP) and Mohamad bin Rashid Al Maktoum Knowledge Foundation (MBRF) launched the Global Knowledge Index (GKI), a tool by which data from 131 countries were collected for seven sectors—one of which was HE. In this paper, an analysis of the HE index data is introduced. Then, three key issues which emerged from data are discussed. The first issue is HE efficiency, which is measured by comparing the indexes of HE inputs and outputs. The second issue is the enabling environment factors that might support or limit the growth of HE. The third issue is the intricate relationship between HE, economy, and Research and Development (R&D). The study found that HE efficiency is declining globally except in a few areas. A strong positive relationship was found between the enabling environment and variables of political stability and government effectiveness and HE’s ability of knowledge production. Furthermore, strong relationships were found between HE outputs, economy, and R&D respectively. The study concludes with future directions for increasing the quality of HE.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.030 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".