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Abstract:

Drawing on four well‐established theoretical perspectives, this paper proposes an intersectional, emotionally grounded framework for understanding how gender and age jointly shape entrepreneurial perceptions across psychological, social, and cultural domains. Using 2024 Italian Global Entrepreneurship Monitor data and ordinal logistic regression, we find that women are approximately 30% more likely to report fear of failure and 20% less likely to feel self‐efficacious, despite perceiving high social respect for entrepreneurs. These emotional constraints persist across age, suggesting that gendered affective barriers are stable over time. We also demonstrate that composite indices, commonly used in entrepreneurial research, partially obscure gendered nuances that are better revealed through disaggregated analysis. This study contributes to entrepreneurship theory by integrating emotional risk into socially embedded models of entrepreneurial cognition. By addressing gendered emotional barriers, we contribute to building more socially sustainable and resilient entrepreneurial ecosystems, in line with the UN Sustainable Development Goals (SDGs). We further frame these barriers as strategic obstacles to environmentally responsible entrepreneurship, underscoring their relevance to SDGs 5, 8, 9, and 12. Finally, we highlight managerial implications: Incubators, accelerators, and firms should design gender‐sensitive interventions, such as inclusive training, mentorship, and financial instruments, to foster innovation, responsible production, and the resilience of entrepreneurial ecosystems.

Abstract:

Understanding black hole–galaxy co-evolution and the role of active galactic nucleus (AGN) feedback requires complete AGN samples, including heavily obscured systems. Such sources are key to constraining the black hole accretion rate density over cosmic time, yet they are challenging to identify and characterize across most wavelengths. In this work, we present the first ultraviolet (UV) line-selected ([Ne v] Å and C iv Å) sample of obscured AGN with full X-ray-to-radio coverage, assembled by combining data from the Chandra COSMOS Legacy survey, the COSMOS2020 UV–NIR catalogue, mid- and far-IR photometry from XID+, and radio observations from the Very Large Array and MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) surveys. Using cigale to perform spectral energy distribution (SED) fitting, we analyse 184 obscured AGNs at and , enabling detailed measurements of AGN and host-galaxy properties, and direct comparison with simba hydrodynamical simulations. We find that X-ray and radio data are essential for accurate SED fits, with the radio band proving critical when X-ray detections are missing or in cases of poor IR coverage. Comparisons with matched non-active galaxies and simulations suggest that the [Ne v]-selected sources are in a pre-quenching stage, while the C iv-selected ones are likely quenched by AGN activity. Our results indicate that [Ne v] and C iv selections target galaxies in a transient phase of their co-evolution, characterized by intense, obscured accretion, and pave the way for future extensions with upcoming large area high-z spectroscopic surveys.

Abstract:

This study offers a comprehensive bibliometric and text‐mining overview of two decades of sustainability‐oriented entrepreneurship research. Drawing on 7563 peer‐reviewed articles from the Web of Science Core Collection, we map the field's evolution, thematic structure, and disciplinary convergence, identifying influential authors, networks, and journals. Using rule‐based classification and unsupervised learning, we categorize contributions within a four‐pillar framework encompassing environmental, social, economic, and cultural dimensions and examine their prevalence, overlap, and temporal trends. The results reveal a pronounced shift toward transdisciplinarity: 77% of articles engage with at least three pillars, and 34.5% address all four simultaneously. Building directly on this empirical evidence, we propose the Four‐Pillar Intersectionality Framework (F‐PIF), which reconceptualizes sustainable entrepreneurship as a transdisciplinary knowledge domain shaped by interdependent sustainability logics. The F‐PIF is therefore both derived from and 91̽��ed by the bibliometric findings, providing an empirically grounded conceptual model that advances theoretical understanding and offers practical guidance for scholars and practitioners navigating entrepreneurship in the age of sustainability.

Abstract:

The joint capabilities of the Hubble Space Telescope (HST) and JWST allow for an unparalleled look at the early lives of star clusters at near- and mid-infrared wavelengths. We present here a multiband analysis of embedded young stellar clusters in 11 nearby, star-forming galaxies, using the PHANGS-JWST and PHANGS-HST data sets. We use the Zooniverse citizen science platform to conduct an initial by-eye search for embedded clusters in near-UV/optical/near-infrared images that trace stellar continuum emission, the Paschenα and Hα recombination lines, and the 3.3 μm polycyclic aromatic hydrocarbon feature and its underlying continuum. With this approach, we identify 292 embedded cluster candidates for which we characterize their ages, masses, and levels of line-of-sight extinction by comparing the photometric data to predictions from stellar population models. The embedded cluster candidates have a median age of 4.5 Myr and an average line-of-sight extinction 〈AV〉 = 6.0 mag. We determine lower limits on source stellar masses, resulting in a median stellar mass of 103 M⊙. We use this sample of embedded cluster candidates to train multiple convolutional neural network models to carry out deep transfer learning-based searches for embedded clusters. With the aim of optimizing models for future catalog production, we compare results for four variations of training data using two neural networks. Confusion matrices for all eight model configurations, as well as inter-model identification trends, are presented. With refinement of the training sample, we determine that optimized models could serve as a pathway for future embedded cluster identification beyond our 11 galaxy sample.