Academic promotion requires evaluators to translate heterogeneous evidence about a candidate into a single consequential decision. Research has documented which achievements count in this process, but has paid less attention to whether the presentation through which achievements are reviewed conditions their evaluative value. We examine this question using archival data from 361 faculty members who applied for promotion at a Chinese research university. Publication productivity and government grant productivity were constructed from administrative records. A large multimodal model for visual scoring and latent Dirichlet allocation topic modelling measured the visual and textual characteristics of candidates’ PowerPoint presentations, respectively. Logistic regression results show that both forms of research achievement are positively associated with promotion. This association, however, varies with the presentation. Mean aesthetic quality positively moderates the associations of publications and grants with promotion, although the publication interaction is only marginally significant when all interactions are estimated together. Aesthetic fluctuation weakens the grant-promotion association but not the publication-promotion association. Information richness shows an asymmetric pattern: it weakens the publication-promotion association and strengthens the grant-promotion association. Theoretically, the study develops a representation-dependent account of commensuration by showing that presentation characteristics condition how heterogeneous achievements are translated into a common promotion decision. Practically, the findings suggest that candidates should match deck design to the informational demands of their records and that universities should use standardized achievement dossiers and criterion-specific ratings to support comparability.
Making the move to promotion is a major turning point in faculty lives, however, we know little of the cognitive and interpretive processes that faculty members use in making sense of academic promotion systems in general. This paper has sought to bring to light, in part, the cognitive and interpretive aspects of academic promotion at Makerere University, Uganda. The theoretical underpinning of this paper has been based on the hermeneutic philosophy of Paul Ricoeur, and the design of the study has been narrative inquiry. Data collection was from thirty-three respondents during interview sessions that included academics from across a broad spectrum of departments, disciplines, promotion stages, promotion status and academic title. This study has shown that there is a cognitive and interpretive dimension to academic promotion experiences. The findings include the prefigurative suspicion of promotion that results from a shadow text of promotion policies existing alongside official policies and the narratives that promote arbitrariness and nepotism in the promotion process and the refiguration of cynical interpretations of the promotion processes in the form of withdrawal, indifference and strategic compliance.
This study develops and validates the Faculty Internal Governance Quality Scale (FIGQS), a measurement instrument designed to assess governance quality within centralized and affiliating university systems. Existing governance measures are largely rooted in Western higher education contexts characterized by decentralized and shared-governance traditions, limiting their applicability to highly centralized systems common in South Asia. Grounded in Organizational Justice Theory, the study conceptualizes Internal Governance Quality as a multidimensional construct comprising participation in decision making, transparency, accountability, and communication, reflecting faculty experiences of governance processes. Rather than constituting a culturally distinct measurement approach, the FIGQS’s primary contribution lies in validating this Western-originated theoretical framework within a structurally different, centralized governance context. A sequential multi-phase research design was employed, including conceptual development, expert-based content validation, exploratory factor analysis, and confirmatory factor analysis. Survey data were collected from 855 full-time faculty members across seven universities affiliated with Bangladesh’s National University system, providing adequate power for robust model estimation. Results supported a second-order reflective model with acceptable model fit (CFI = 0.918, TLI = 0.906, RMSEA = 0.08) and strong psychometric properties (composite reliability > 0.80; average variance extracted > 0.50). The FIGQS contributes theoretically by extending Organizational Justice Theory to institutional governance, methodologically by offering a validated scale for centralized higher education systems, and practically by supporting evidence-based governance evaluation and reform aligned with Sustainable Development Goal 16.
This article examines the legal foundations and practical realities of academic freedom in Austria, situating its analysis within the broader European context. Austria’s robust constitutional protection of academic freedom, rooted in Article 17 of the Basic Law of 1867, is complemented by European legal safeguards and the explicit autonomy granted to public universities under Article 81c of the Federal Constitutional Law. The study clarifies conceptual distinctions between academic freedom, freedom of scientific research, and institutional autonomy, referencing contemporary European models and legal interpretations. Despite Austria’s high international ranking for academic freedom, recent developments have introduced new tensions. These challenges manifest in areas such as university governance, third-party funding, the balance between accountability and autonomy, and slightly increased political polarisation. The article highlights the importance of academic integrity, transparency, and public trust in science, noting positive legal reforms and increased awareness among stakeholders. Inspired by Popper’s critical rationalism, the paper underscores the intrinsic link between science and democracy, advocating for universities as centres of contestable ideas and rational discourse. The Austrian experience offers valuable insights for the evolving European doctrine of academic freedom, emphasising the need for vigilance against gradual erosion and the collective responsibility to safeguard academic freedom as a cornerstone of liberal democracy.
Universities worldwide are facing a practical governance challenge as generative artificial intelligence tools become widely available to students and staff, while institutional responses remain fragmented across academic integrity rules, assessment design, research governance, and inconsistent understandings of acceptable assistance. This paper presents a pragmatic institutional toolkit for translating broad principles for ethical generative artificial intelligence use into workable higher education practice. Using a document-based design, the study draws on institutional evidence from a public higher education institution in Botswana, including governance documents, policy texts, role-specific guidelines, sensitisation plans, and workshop artefacts. The analysis shows that generative artificial intelligence governance can be operationalised without creating a standalone policy by embedding definitions and expectations within existing academic integrity and misconduct frameworks, developing separate guidance for different user groups, and using workshops, institutional memory and detection-tool training as educative and procedural mechanisms. The paper synthesises these elements into a roadmap-to-practice translation architecture, comprising five recurring translation moves, seven canonical toolkit components, and a six-step implementation pathway that emphasise transparency, proportionality, documentation, and due process. Rather than claiming effectiveness outcomes beyond the institutional record, the study offers a documented design case and an adaptable model for universities seeking coherent and feasible approaches to generative artificial intelligence governance, particularly in resource-constrained contexts.
