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The phylogenesis of palaeoarts, and their coevolution in relation to artification
(The University of Waikato, 2026) Allegretti, Pietro; Kingsbury, Justine; Killin, Anton; Pievani, Dietelmo
In this thesis, I investigate the hypothesis of artification developed by Ellen Dissanayake and its relevance for developing an account of the evolutionary trajectory of art-making in humans and hominins. The debate about the evolution of art-making has produced many theories of how arts evolved in humans’ deep past. One of the most promising views is the hypothesis of artification, that suggests that artworks evolved in ritual ceremonies as an enactment of the hominin predisposition to make bodies, objects, and vocalisations perceptually striking. I suggest that artification’s antecedents can be traced back to Australopithecines’ mother-infant interaction. I provide an argument in support of characterising artification as a cognitive toolkit that first evolved as an attention-getter mechanism, and art-making as a set of culturally accumulated practices exploiting this toolkit that evolved following coevolutionary trajectories with socio-cognitive niches in later hominins. I conclude that artification has a mosaic evolution, and that the first instances of art-making can be tracked back to the Early-Acheulean ≈ 1.7 mya.
Truth as a cultural value
(Taylor and Francis Group, 2026) Wyatt, Jeremy
Gila Sher (2025) puts forward a theory of truth that is founded on a thought-provoking strategy for thinking about truth’s nature. According to this strategy, in attempting to develop an account of truth’s nature, we should foreground the fact that truth is an intrinsic human value. In this brief commentary, I will argue that truth is best described as a cultural, rather than a human, value. I will then explore the ramifications that this has for Sher’s strategy for thinking about truth’s nature.
Assessing machine learning models for near-infrared regression by measuring stability towards diffeomorphisms
(Elsevier, 2025) Wohlers, Mark W.; McGlone, V.A.; Frank, Eibe; Holmes, Geoffrey
Near infrared (NIR) spectroscopy is widely used as a tool for non-destructive assessment of fruit quality by applying measured spectra to predict quality parameters such as dry matter and soluble solids content using a suitable regression method. With continued advancements in deep learning, there is potential for improved predictive performance when neural network models are applied instead of partial least-squares regression, but choosing a model remains challenging as performance is sensitive to the model's architecture. Taking inspiration from work done in image classification, we propose model selection by assessing relative stability to diffeomorphic transformations, providing a complementary approach to standard validation methods. This is particularly useful when labelled validation data is limited. Our empirical results on several NIR regression problems indicate that the proposed approach is comparable to the use of independent validation sets. In addition to the choice of deep learning architecture, we also consider the selection of the number of components in partial least-squares regression to demonstrate the method's generality.
On the precipitability of binary Ti alloys bearing 4-period d-metal eutectoid stabilisers
(Elsevier, 2026-06) Bolzoni, Leandro; Yang, Fei
There is no specific, simple approach for predicting whether the addition of an eutectoid beta stabiliser to Ti leads to an ‘active’ eutectoid transformation upon primary processing (e.g., sintering). Here, we demonstrate that, among theoretical/empirical models, phase diagram features, and electronic structure parameters, the hypoeutectoid area is the best predictor, followed by the molybdenum equivalent parameter (MoE), of the precipitability for 4-period d-metals. As the area increases, which corresponds to the addition of progressively stronger β-eutectoid stabilisers, the less active the eutectoid phase transformation, changing from pearlitic to bainitic (i.e., need for an ageing heat treatment). This occurs if the MoE weighted coefficient is, respectively, lower than or higher than 1. This is because molybdenum is taken as reference, and the higher the coefficient, the larger the drop of the β transus temperature (i.e., higher stabilisation). Valid for 4-period d-metals, it remains to be proven for 5-period and 6-period d-metals.
Vicarious learning in SME internationalization: A systematic review and thematic synthesis
(Cambridge University Press, 2026) Perera, Shanika Rangani; Sinha, Paresha N.; Gilbert-Saad, Antoine; Gajanayaka, Channa
Vicarious learning helps small and medium-sized enterprises (SMEs) acquire foreign market knowledge by observing and interpreting other firms’ actions and outcomes in international markets. We searched Scopus, ProQuest, and EBSCOhost (2000–2025) and retained 27 studies (2007–2025). The synthesis organizes prior studies into four analytically derived categories that summarize how vicarious learning is conceptualized and operationalized in SME internationalization research: (T1) peer performance benchmarking, (T2) imitation and leader-following, (T3) institutional mimetic pressures, and (T4) network-, cluster-, and advisor-enabled vicarious learning. Across themes, a subset of studies suggests that absorptive capacity may condition whether external experience is recognized, assimilated, and exploited, although direct tests remain uneven and in some cases the contingency is inferred rather than explicitly tested. We translate these insights into an organizing framework and a future agenda on boundary conditions, measurement, and multi-level designs, positioning the review as mechanism clarification that imposes conceptual order on a fragmented literature, rather than as field-level consolidation.