Assessing data diversity and model fairness in machine learning (ML) requires access to sensitive demographic attributes, which are often unavailable due to privacy constraints. While several methods have been proposed to estimate these properties, the field lacks a unified and reproducible evaluation framework. To fill this gap, we introduce Proxy-based Assessment for Inclusion, Representation, and Equity (PAIRE), a standardized benchmark for evaluating Fairness and Diversity (FD) estimators that operate without individual-level sensitive attributes. Leveraging PAIRE, we evaluate state-of-the-art demographic estimators on binary classification (UCI Adult, 45k instances) and multiclass ranking (TREC Fair Ranking, 1.15M instances, 21 regions), measuring estimation accuracy and vulnerability to attribute inference. Advanced methods demonstrate superior diversity estimation, reducing estimation error by up to 81%. However, this performance can be inverted in fairness assessment, with naive counting-based methods achieving up to 41% lower error than advanced quantification-based estimators, highlighting that strong performance on direct prevalence estimation does not guarantee reliability for downstream fairness assessment. Finally, privacy attacks formalized with PAIRE highlight that aggregate demographic estimators can be exploited to infer individual sensitive attributes with high accuracy (F1macro>0.9). Overall, PAIRE establishes a challenging benchmark for attribute-unaware FD estimation, providing a holistic evaluation in sensitive applications.
The origin of obscuration in active galactic nuclei (AGN) is still a matter of contention. It is unclear whether obscured AGN are primarily due to line-of-sight effects (Orientation model), a transitory, dust-enshrouded phase in galaxy evolution (Evolution models), or a combination of both. The role of an inner torus around the central supermassive black hole also remains unclear in pure Evolution models. We use cosmological semi-analytic models and semi-empirical prescriptions to explore obscuration effects in AGN at cosmic noon, in the range 1 < z < 3. We consider a realistic object-by-object modelling of AGN evolution including different AGN light curves (LCs) composed of phases of varying levels of obscuration, usually (but not uniquely) with a larger degree of obscuration before the peak of AGN activity, mimicking the possible clearing effects of strong AGN feedback. Evolution models characterized by AGN LCs with relatively short pre-peak obscured phases followed by more extended optical/ultraviolet (UV) visible post-peak phases, struggle to reproduce the high fraction of obscured AGN at z similar to 2-3 inferred from X-ray surveys. Evolution models characterized by AGN LCs with sharp post-peak declines or persistent or multiple obscuration phases are more successful, although they still face challenges in reproducing the steady drop in the fractions of obscured AGN with increasing luminosity measured by some groups. Invoking a fine-tuning in the input LCs, with more luminous AGN defined by longer optical/UV visible windows, can improve the match to the decreasing fractions of obscured AGN with luminosity. Alternatively, a long-lived central torus-like component, with thickness decreasing with increasing AGN power, naturally boosts the luminosity-dependent fractions of obscured AGN, suggesting that small-scale orientation effects may still represent a key component even in Evolution models. We also find that in our models major mergers and starbursts, when considered in isolation, fall short in accounting for the large fractions of highly obscured faint AGN detected at cosmic noon.
The present paper addresses some foundational issues on the status of parametric linguistics, understood as a partially independent new branch of formal grammar and of the cognitive sciences more generally. Chomsky’s (1964) original three levels of adequacy are extended to five and it is then suggested that the theory of parameters, being able to deal with cultural variation, is in the best position to achieve adequacy at the fourth proposed level, one connected with historical explanations, and some methods to pursue this goal are proposed. The development of parametric linguistics is viewed as a major step toward the potential application of the Galilean style of formal grammar both to the study of linguistic history and to other domains of cognitive science and cultural anthropology.
PurposeThis study explores the growing need for empirical research on workplace agility and its impact on employee behaviour. Grounded in the Job Demands-Resources theory, the research examines the relationship between agile work methods and innovative work behaviour, with the mediating role of job autonomy.Design/methodology/approachThe study employs a quantitative research design based on survey data collected from large firms in Italy in 2024. Regression analysis was used to test the relationships between agile work methods (the independent variable), job autonomy (the mediator) and innovative work behaviour (the dependent variable).FindingsThe results support the proposed model, showing job autonomy mediates the relationship between agile work methods and innovative work behaviour. The findings highlight agile work methods lead to greater job autonomy among employees, which in turn enhances their ability to engage in innovative work behaviour. Results underscore the importance of aligning individual and organisational conditions to maximise the benefits of agility.Originality/valueThis study contributes to innovation management and organisational studies by (1) developing and validating the agile work methods scale, conceptualising them as different from agile work practices, (2) identifying job autonomy as a mediating mechanism between agile work methods and innovative work behaviour based on the Job Demands-Resources theory, (3) enriching supportive evidence on the interactionist theory of creativity and innovation with evidence on agility and the role of autonomy. The research provides new insights into how organisations can leverage agile work methods to foster innovative work behaviour among employees.
Although innovation is often portrayed as arising deterministically from deliberate strategy and calculated decisions, many significant breakthroughs emerge not from planning but serendipitously. Building on this insight, this paper bridges the literatures on dynamic capabilities and serendipity to examine how SMEs realize serendipitous value during digital transformation (DT). Drawing on 21 semi-structured interviews with 11 top managers from four digitally transformed manufacturing SMEs, it was explored how these companies attempted to navigate the serendipity journey, comprising triggering, association, materialization, and realization. The findings show that cospecialization, a microfoundation of dynamic capabilities, conditions the unfolding of serendipitous events in the context of DT. Cospecialization fosters the coordinated association of complementary assets, resources, and capabilities in the wake of change driven by digital technologies, enabling businesses to sustain momentum from initial triggers through the materialization and realization of serendipitous opportunities. We develop a process model to illustrate how SMEs harness situated agency to move from unexpected triggers to realized value through the unfolding dynamics of the serendipity journey.
