A Study on the Experiences of Higher Education Good Governance in European and North America Countries; Some Lessons Gained for Higher Education in Iran
Bibliographic record
Abstract
Higher education, in order to achieve its strategic goals in society, requires governance understanding in a sustainable trend so that it can correctly define and outline its mission and step towards it firmly. Research knowledge and experiences of the advanced countries in this field may certainly contain some considerable and –sometimes - valuable lessons gained in policies for higher education systems in other countries, especially for Iranian higher education system suffering from some ineffective traditional procedures and avoiding up-to-date governance methods. In this article, by applying the research method of document analysis and using the purposeful sampling method, the identification and qualitative analysis of good governance experiences in higher education of three selected countries - including Germany, England and Canada - has been addressed in the four measures of quality affairs, academic independence and financial management. Data gatherings were done by searching contents of universities’ websites, published articles, essays, reports, governmental and academic manuscripts. Data was qualitatively analyzed at two levels of open and axial coding. From the study and analysis of the experiences of good governance in higher education of selected countries, in the dimension of quality measures, including evaluation and quality assurance in the context of agreed frameworks, multiple use of the evaluation system, validation and quality assurance, transparency and accountability, and improvement were identified as the logic of evaluation and quality assurance of higher education. Systematic synergetic management and planning, and network management based on scientific reference were experiences that were represented in the dimension of university management and planning. Morover, in the financial planning measures, experiences such as contingency budgeting based on scientific indicators, and diversity of support in providing financial resources were identified. In the end, the article, by enumerating the findings obtained from its research in the experiences of the governance of the selected countries, has recommended to all the policy makers and officials of the country's higher education to study and review them as lessons that can be learned for awareness and to create a well-considered platform in order to use them effectively, in the framework of appropriate and updated action plans in the field of governance of higher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".