A Study on the Experiences of Higher Education Good Governance in European and North America Countries; Some Lessons Gained for Higher Education in Iran
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,009 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».