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Collegiality is the modus operandi of universities. Collegiality is central to academic freedom and scientific quality. In this way, collegiality also contributes to the good functioning of universities’ contribution to society and democracy. In this concluding paper of the special issue on collegiality, we summarize the main findings and takeaways from our collective studies. We summarize the main challenges and contestations to collegiality and to universities, but also document lines of resistance, activation, and maintenance. We depict varieties of collegiality and conclude by emphasizing that future research needs to be based on an appreciation of this variation. We argue that it is essential to incorporate such a variation-sensitive perspective into discussions on academic freedom and scientific quality and highlight themes surfaced by the different studies that remain under-explored in extant literature: institutional trust, field-level studies of collegiality, and collegiality and communication. Finally, we offer some remarks on methodological and theoretical implications of this research and conclude by summarizing our research agenda in a list of themes.

The purpose of this study is to explore and prioritize the factors that determine the social insurance contribution of unorganized workers.

A two-stage procedure was adopted to recognize and prioritize factors influencing the social insurance participation of unorganized workers: first, crucial factors influencing unorganized workers’ contribution towards social insurance were identified by employing exploratory factor analysis, and in the second phase, the fuzzy analytical hierarchal process was applied to rank the specified criteria and then sub-criteria by assigning weights.

Four broad factors were identified, namely, economic, political, operational and socio-psychological, that significantly influence unorganized workers’ contribution towards social insurance. Later findings revealed that the prime influencer of unorganized workers’ contribution is employment contracts followed by average earnings, delivery of quality services, eligibility and accessibility.

The research findings are feasible as the basic propositions are based on real-world scenario. The identification and ranking of factors have the potential to be used as a checklist for policymakers when designing pension and social insurance for unorganized workers. If it is not possible to consider all, the criteria and sub-criteria assigned upper rank can be given priority to extend pension coverage for a large group of working poor.

The key factors driving social insurance contributions have been highlighted by studying the stakeholders’ perceptions at a micro level. By comprehending the challenges, there is a possibility of covering a large section of the working poor into social insurance coverage.

This paper is believed to be one of its kinds to acknowledge a combination of factors that determine the contribution of unorganized workers to social insurance. This study is an empirical investigation to prioritize the essential drivers of social insurance participation by low-income cohorts in the context of emerging countries. The present approach of employing fuzzy logic has also very limited use in social insurance literature yet.

Artifacts are rarely used today to visualize thoughts, insights, and ideas in strategy work. Rather, textual and verbal communication dominates. This is despite artifacts and visual representations holding many advantages as tools to create and make sense of strategy in teamwork. To advance our understanding of the benefits of visual aids in strategy work, I synthesize insights from cognitive psychology, neuroscience, and management research. My analysis exposes distinct neurocognitive advantages concerning attention, emotion, learning, memory, intuition, and creativity from visual sense-building. These advantages increase when sense-building activities are playful and storytelling is used.

As a result of the COVID-19 pandemic, teacher education in Qatar, similar to many countries around the world, witnessed a succession of disruptions to the way it operated. The disruption continued throughout much of 2020, and the need to adapt to arising changes and concerns permeated all aspects of teacher education, particularly the practicum experience. The chapter presents our attempt to investigate the influence of an adapted practicum experience which was based on the synthesis of qualitative evidence (SQD) model on the development of six student teachers’ technology knowledge and skills. Using a qualitative case study research design, we collected data from multiple data sources, including pre–post-interviews and weekly reflection logs. Quantitative data collected from a pre–post-administration of the SQD survey and TPACK (Technological Pedagogical and Content Knowledge)-practical survey were used to triangulate the qualitative data. Findings from the thematic analysis and descriptive statistical analysis revealed evidence for participants’ increased TPACK-practical knowledge and skills, specifically in the domains of practical teaching and curriculum design. However, an emerging theme revealed that participants considered technology before pedagogy during instructional design. Findings also revealed two challenges to participants’ further development, namely working within a restricted learning environment and experiencing limited mentoring opportunities. We illustrated several implications for the design of the practicum experience and the required institutional support within the context of continued disruption to education and thereafter.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.