Search

Although university’s contribution to the democratic society has been studied adequately, the establishment of its internal democratic institutions has not. Issues of autonomy and accountability exist whereas, today’s Postmodernism introduces further uncertainty. After constructing a framework for measuring democracy within a university using democracy indicators selected from international organizations, we attempt to interrelate these indicators to its democratic characteristics, raising the question: “To what extent could these characteristics be eroded before the university collapses?” Interviews with European academics were conducted and the influence of forces external to the university were studied using the Central European University in Hungary as a case study. The findings show that increased state control undermines institutional autonomy and so does imposing unnecessary restrictions. Protecting democracy and academic freedom, civil rights, and supporting an open society are of paramount importance, otherwise the university collapses. A model that captures such catastrophic state changes is finally proposed.

This paper proposes the establishment of a systematic framework for measuring dimensions of democracy within a university. It is shown that certain indicators internationally established and validated for the measurement of the institutional characteristics of democracy can, through appropriate adaptation, be transferred to the university field. A new model of mapping these characteristics to the university field was built using all available knowledge from international surveys, valid databases and researches conducted using questionnaires and structured interviews, specifically for the purposes of this paper. Chord diagrams, capable of providing meaningful dynamic graphical representations, were employed as a novel evaluative tool that highlights the existence and degree of significant interrelations between democracy indicators and democratic characteristics of the university. As a case study, we have chosen to analyze, compare and contrast the three most representative laws governing the operation of Greek universities for the last 35 years using the proposed model. Its operation, validity and capabilities were verified, after encapsulating the complex interdependencies among its variables, for a large time period, influenced by adverse social and economic changes. Thus, it can be used as an effective tool for comparisons among different universities, with respect to their democratic constitution.

To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.

This article attempts a reading of Andreas M. Kazamias’s work and method as a persistent and firmly grounded attempt to “go against the tide” of an empirical/instrumentalist comparative education a...

Andreas Kazamias is professor of comparative education at the University of Madison, Wisconsin, and professor emeritus at the University of Athens. He was a founding member of the Comparative and International Education Society of North America (CIES). He is a member of the Athens Academy and a honorary fellow of the Comparative and International Education Society.