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Codification of scholarly values in academic publishing: AI governance and institutional tensions
(Assumption University, 2026) Duc, Anthony Le
This article examines the current artificial intelligence (AI) policies by five prominent academic publishers—Elsevier, Springer Nature, Taylor & Francis, SAGE Publishing, and Cambridge University Press— interpreting them as efforts to codify values as generative AI becomes embedded in academic communication. Through comparative qualitative analysis, the study identifies core values consistently operationalized across these policies, including exclusive human authorship, mandatory disclosure, non-delegable accountability, and confidentiality in peer review. As technological change disrupts traditional norms, these policies act as both regulatory tools and ethical anchors, stabilizing the identity and legitimacy of academic institutions. However, this codification creates structural tensions. Turning ethical principles into formal rules produces unintended consequences, from adding new layers of scholarly labor to reinforcing existing institutional inequalities. In addressing both epistemic risks and the tensions that arise from governance responses, the article advocates for AI governance that is reflexive, inclusive, and context-sensitive. Such models should preserve institutional integrity while respecting the evolving partnership between humans and machines in the production of knowledge.
A new macroflora from the Turonian–Coniacian Moreno Hill Formation, New Mexico, USA
(Elsevier Masson s.r.l., 2026) Cilliers, Charl D.; Contreras, Dori L.; Tucker, Ryan T.; Roychoudhury, Alakendra N.; Zanno, Lindsay E.
The Moreno Hill Formation of the Salt Lake Coal Field (New Mexico, USA) preserves terrigenous sediments deposited from ∼90.9 until after 88.6 Ma, a critical window of climatic recovery following the ∼94.5 Ma peak of the Cretaceous Thermal Maximum. Recent fieldwork uncovered new macrofloral assemblages: a lower member flora derived from a paralic delta plain, with carbonaceous, pedogenetically altered floodplain sediments indicating often waterlogged conditions, and an upper member flora uncovered from slightly drier, more distal floodplain sediments. We report 18 distinct foliar morphotypes from the study of 116 leaf specimens. Dicot angiosperms are the most diverse in both assemblages, including platanoids, Saliciphyllum, and Cinnamomoides. While only two dicot morphotypes are shared between the lower and upper assemblages, two conifers (Brachyphyllum and taxodiaceous Cupressaceae indet.) are found in both. The fossils at both sites were collected from overbank deposits and lack evidence for significant transport; thus, we infer that the floodplains adjacent to channels during this interval were occupied by a mixed angiosperm-conifer woodland ecosystem. Although the two assemblages are separated by ∼1.5 to 2 million years, they reflect similar floodplain woodlands on a changing landscape in which the Western Interior Seaway was regressing and the local conditions drying. Sedimentological, leaf physiognomic, and wood growth-ring data collectively support persistent warm, yet gradually more seasonal conditions after the thermal maximum. This first report of Moreno Hill Formation leaf macrofossils establishes a regional Turonian–Coniacian palaeobotanical baseline for future studies of vegetation and ecosystem change in the southern Western Interior.
Spectral assessment of nutrient limitation in the savanna landscape: selection of spectral indices towards Sentinel-2 upscaling
(Frontiers Media SA, 2026) Ngcoliso, Nasiphi; Gxokwe, Siyamthanda; Ramoelo, Abel; Tsele, Philemon; Qabaqaba, Mcebisi
Nutrient limitations can significantly impact the ecosystem services provided by the savanna biome, potentially leading to degradation and reduced grazing capacity if not detected in time. A key indicator of growth-limiting nutrients is the Nitrogen to Phosphorus (N:P) ratio. However, grass foliar phosphorus content has rarely been studied in African savannas, especially using remote sensing approaches. As a result, there is limited information on the spatial distribution of nutrient limitations in these ecosystems. This study aimed to develop a Sentinel-2-based machine learning regression model to predict and map the distribution of the N:P ratio in the northern region of Kruger National Park (KNP), South Africa, which is dominated by the savanna rangeland biome. Fieldwork was conducted between 15 March and 30 April 2008 to collect grass samples and spectral data using an Analytical Spectral Device (ASD). The hyperspectral field data were then resampled to match the multispectral configuration of Sentinel-2 imagery. A Random Forest Regression (RFR) technique was applied to the simulated Sentinel-2 datasets to develop predictive models of the N:P ratio. Model accuracy was evaluated using the Root Mean Square Error (RMSE) Relative Root Mean Square Error (RRMSE), Percent Bias (PBIAS), and the coefficient of determination (R2). The results showed that vegetation indices (VIs), particularly the Normalized Difference Red Edge (NDRE) derived from Sentinel-2 bands B8 and B5, was optimal for estimating N:P ratio. This index explained over 80% of the N:P variability, with the lowest PBIAS of 0.02%. The best-performing model was used to map nutrient limitations across the study area using Sentinel-2 imagery. The spatial analysis indicated consistent nitrogen limitation and co-limitation across the investigated regions, with no evidence of phosphorus limitation. The high-accuracy models demonstrate the effectiveness of Sentinel-2 imagery for estimating nutrient limitations in heterogeneous savanna landscapes. This study offers a cost-effective, scalable tool for decision-makers involved in the management, sustainability, and restoration of the savanna biome. Future research should consider incorporating textural and environmental variables to enhance model performance and understanding of nutrient dynamics.
Reframing interprofessional competencies through Ubuntu: a decolonial proposal
(Taylor and Francis Ltd., 2026) Filies, Gerard C.; Africa, Luzaan
What was the educational challenge? Global interprofessional education (IPE) competency frameworks have advanced collaboration in health professions education, but remain rooted in predominantly Western epistemologies. Frameworks emphasise individual competence, role delineation, and performance, often overlooking relational, communal, and contextually grounded ways of knowing. This creates a misalignment in Global South contexts, particularly in Africa, where care is embedded in collective identities and social interdependence. There is a need to rethink interprofessional competencies to better reflect diverse epistemologies and support equitable, contextually relevant practice. What was the solution and how was it implemented? We propose an Ubuntu-informed reconceptualisation of interprofessional competencies. Ubuntu values and principles reframe collaboration as collective becoming, rather than coordinated individual performance, through relational personhood, communal responsibility, dialogical communication, fluid roles, shared practice, and collective leadership. This reframing repositions interprofessional collaboration as an ethical, relational, and community-oriented practice in the African context. What lessons were learned? This approach offers a decolonial lens for IPE, enhancing cultural relevance in the Global South while deepening relational and equity-oriented practice globally. It strengthens existing frameworks by foregrounding belonging, mutual recognition, and shared humanity. What are the next steps? Future work includes developing Ubuntu-informed competency frameworks, piloting in African health professions education programmes, and evaluating the impacts on student interprofessional identity and collaborative practice.
Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis
(Springer Nature, 2026) Okekunle, Akinkunmi Paul; Kumuthini, Judit; Asowata, Osahon Jeffery
Background: Data science methods can provide novel and pragmatic approaches for preventing and controlling non-communicable diseases (NCDs) in Africa. This study highlights current efforts, opportunities, and challenges in leveraging data science methods to accelerate and advance the prevention and control of NCDs in Africa. Methods: We undertake a systematic review and gap analysis, as registered in PROSPERO (CRD42023406237). Results: Our findings suggest several data science methods have been used in research across the four leading NCDs in Africa. However, limited information exists on their application to improve disease surveillance, risk factor identification and characterization, prevention, treatment, drug discovery and rehabilitation. Machine learning outperforms traditional statistical methods in improving risk stratification in most studies (80.8%) designed for the prevention and control of NCDs. Notwithstanding, most (76.0%) data science techniques for NCDs prevention and control remain in the exploratory research phase, with limited clinical or public health application and minimal impact on the African population. There are critical gaps along the continuum of data generation, data quality, method development, and validation, which may be attributed to inadequate funding, capacity development, policy shortcomings, and infrastructure deficits. Considerable gaps exist in intra-African collaboration, data sharing, and replication, which hinder the cross-cultural replication and applicability of data science methods for NCDs prevention and control in Africa. Conclusions: Multi-sectoral interventions that promote interdisciplinary capacity building, investment, and knowledge linkages, taking into account indigenous epistemologies, are needed to harness the enormous potential of data science to accelerate the prevention and control of NCDs in Africa.