Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, that is, there is no globally interpretable way to understand how a model makes estimates. This is a clear problem for governments who want to evaluate policy as it is difficult to understand whether such models are functioning in ways that are fair, based on the correct interpretation of evidence and transparent enough to allow for accountability if things go wrong. However, there has been little discussion of transparency problems in the causal machine learning literature and how these might be overcome. This article explores why transparency issues are a problem for causal machine learning in public policy evaluation applications and considers ways these problems might be addressed through explainable AI tools and by simplifying models in line with interpretable AI principles. It then applies these ideas to a case study using a causal forest model to estimate conditional average treatment effects for returns on education study. It shows that existing tools for understanding black-box predictive models are not as well suited to causal machine learning and that simplifying the model to make it interpretable leads to an unacceptable increase in error (in this application). It concludes that new tools are needed to properly understand causal machine learning models and the algorithms that fit them.

According to Shelly Kagan, the moral status of an individual is determined by the extent to which the individual has (has now, might/will have, or could have had) certain psychological capacities. Roughly speaking, the greater one's relevant psychological capacities, the higher their moral status. In this paper, I offer a twofold critique of Kagan's hierarchicalism. On the one hand, I argue against the primary argument in favor of Kagan's view (the argument from distribution) by challenging the key intuition on which the argument relies, thereby reducing the appeal of Kagan's position. On the other hand, using Kagan's general methodology, I argue that a good reason to reject Kagan's account of moral status is that he fails to explain away the counterintuitive result of his theory in the case of normal variation.

The development of environmental education (EE) goals has rarely been problematised. To shed light on this process, we focused on EE in the Czech Republic. Non-governmental organisations (NGOs) play a key role there, facilitating the process in coordination with government institutions, schools and for-profit companies. Drawing on three theoretical perspectives that explain the formation of organisational goals (consensus building, community of discourse and practice and governmentality), we examined how different stakeholders contribute to the definition of common goals for EE. Through ethnographic research in an NGO and at EE events, complemented by interviews with lecturers and leaders, our research revealed that despite the high diversity of stakeholder positions and interests, the organisational field of EE is highly inclusive and shows few internal conflicts. Using chosen theoretical perspectives, we explain how vaguely defined common goals and weak manifestations of conflict contribute to the sharing of knowledge, practices and ethical responsibilities in the EE field.

Plasma levels of branched-chain amino acids (BCAA) and their metabolites, branched-chain ketoacids (BCKA), are increased in insulin resistance. We previously showed that ketoisocaproic acid (KIC) suppressed insulin-stimulated glucose transport in L6 myotubes, especially in myotubes depleted of branched-chain ketoacid dehydrogenase (BCKD), the enzyme that decarboxylates BCKA. This suggests that upregulating BCKD activity might improve insulin sensitivity. We hypothesised that increasing BCAA catabolism would upregulate insulin-stimulated glucose transport and attenuate insulin resistance induced by BCKA. L6 myotubes were either depleted of BCKD kinase (BDK), the enzyme that inhibits BCKD activity, or treated with BT2, a BDK inhibitor. Myotubes were then treated with KIC (200 μM), leucine (150 μM), BCKA (200 μM), or BCAA (400 μM) and then treated with or without insulin (100 nM). BDK depletion/inhibition rescued the suppression of insulin-stimulated glucose transport by KIC/BCKA. This was consistent with the attenuation of IRS-1 (Ser612) and S6K1 (Thr389) phosphorylation but there was no effect on Akt (Ser473) phosphorylation. The effect of leucine or BCAA on these measures was not as pronounced and BT2 did not influence the effect. Induction of the mTORC1/IRS-1 (Ser612) axis abolished the attenuating effect of BT2 treatment on glucose transport in cells treated with KIC. Surprisingly, rapamycin co-treatment with BT2 and KIC further reduced glucose transport. Our data suggests that the suppression of insulin-stimulated glucose transport by KIC/BCKA in muscle is mediated by mTORC1/S6K1 signalling. This was attenuated by upregulating BCAA catabolic flux. Thus, interventions targeting BCAA metabolism may provide benefits against insulin resistance and its sequelae.

This article examines how European Union (EU) arms control measures are tailored to its constitutional foundations. EU Member States subject shipments of arms and components to controls so as to screen them for risks and potentially block them. In this context different Member States may make different geopolitical and humanitarian risk assessments. Existing EU measures have achieved only limited security screening harmonisation, and have left room for Member States to shirk their obligations under international humanitarian law. But in case of joint arms production, which the EU subsidises to become more autonomous, one Member State’s arms controls may block another State’s exports and thereby jeopardise cooperation. This article posits that any reform of EU arms controls should start by re-evaluating their present legal basis. A constitutional competence analysis shows that controls on arms shipments to non-EU states should be regulated in part through the Common Commercial Policy (CCP), and not just through the Common Foreign and Security Policy (CFSP). This would be consistent with other EU regulatory regimes for trade security. While a joint CFSP-CCP approach cannot fully prevent conflict, since this would require further foreign policy harmonisation, it could help foster security convergence and strengthen humanitarian due diligence mechanisms.

This article evaluates the implementation of the pedagogical model by Schio and Reis (2024), aimed at promoting ocean citizenship within basic education. The evaluation is based on a pilot project from the 2021/2022 school year, which involved 543 students, aged 10 to 11, from 10 Blue Schools located along the Portuguese coast. This paper reports on phases 3 and 4 of the Design-Based Research cycle, corresponding to the implementation and evaluation phases of the pedagogical model. Preliminary results allowed us to verify the emergence of new knowledge, skills, values, critical thinking and attitudes, reflecting the development of ocean citizenship competencies among students. These outcomes affirm the model’s applicability and its potential to seamlessly integrate ocean citizenship into the basic education curriculum. However, it was observed that the activism dimension requires additional emphasis. Further testing in diverse educational settings is crucial to refine the model, adjust to local nuances and maximise its impact on nurturing future generations committed to ocean sustainability.

Controller synthesis offers a correct-by-construction methodology to ensure the correctness and reliability of safety-critical cyber-physical systems (CPS). Controllers are classified based on the types of controls they employ, which include reset controllers, feedback controllers and switching logic controllers. Reset controllers steer the behavior of a CPS to achieve system objectives by restricting its initial set and redefining its reset map associated with discrete jumps. Although the synthesis of feedback controllers and switching logic controllers has received considerable attention, research on reset controller synthesis is still in its early stages, despite its theoretical and practical significance. This paper outlines our recent efforts to address this gap. Our approach reduces the problem to computing differential invariants and reach-avoid sets. For polynomial CPS, the resulting problems can be solved by further reduction to convex optimizations. Moreover, considering the inevitable presence of time delays in CPS design, we further consider synthesizing reset controllers for CPS that incorporate delays.

The walk matrix associated to an $n\times n$ integer matrix $\mathbf{X}$ and an integer vector $b$ is defined by ${\mathbf{W}} \,:\!=\, (b,{\mathbf{X}} b,\ldots, {\mathbf{X}}^{n-1}b)$. We study limiting laws for the cokernel of $\mathbf{W}$ in the scenario where $\mathbf{X}$ is a random matrix with independent entries and $b$ is deterministic. Our first main result provides a formula for the distribution of the $p^m$-torsion part of the cokernel, as a group, when $\mathbf{X}$ has independent entries from a specific distribution. The second main result relaxes the distributional assumption and concerns the ${\mathbb{Z}}[x]$-module structure.

The motivation for this work arises from an open problem in spectral graph theory, which asks to show that random graphs are often determined up to isomorphism by their (generalised) spectrum. Sufficient conditions for generalised spectral determinacy can, namely, be stated in terms of the cokernel of a walk matrix. Extensions of our results could potentially be used to determine how often those conditions are satisfied. Some remaining challenges for such extensions are outlined in the paper.

By examining social media interactions, the analysis that is presented in this article reveals how hashtags are adeptly used to reframe the lithium mining issue, embedding it within wider narratives. The article investigates narratives surrounding lithium mining protests in Serbia, using digital ethnography and narrative analysis to study the discourse of ecology activists on the social platform X (formerly known as Twitter). It illuminates the fluid, rhizomatic, and puzzle-like nature of hashtags that helps to achieve online visibility, mobilize audiences for street protests, and appear as narrative building blocks. Hashtags operate as algorithmic signifiers that create additional layers of meaning and fine-tune narratives toward either the left or right side of the political spectrum. This article focuses on how activists use hashtags not just as tools for categorizing content but also as essential components in shaping their narratives. This approach reveals the dynamic engagement of a broad political spectrum in the lithium mining debate, forging connections between different actors. The analysis demonstrates how interconnected hashtags modulate the narratives so that they can transgress from the right to the left side of the political spectrum, indicating that lithium mining is a global rather than a local problem.

This paper aims at exploring the dynamic interplay between advanced technological developments in AI and Big Data and the sustained relevance of theoretical frameworks in scientific inquiry. It questions whether the abundance of data in the AI era reduces the necessity for theory or, conversely, enhances its importance. Arguing for a synergistic approach, the paper emphasizes the need for integrating computational capabilities with theoretical insight to uncover deeper truths within extensive datasets. The discussion extends into computational social science, where elements from sociology, psychology, and economics converge. The application of these interdisciplinary theories in the context of AI is critically examined, highlighting the need for methodological diversity and addressing the ethical implications of AI-driven research. The paper concludes by identifying future trends and challenges in AI and computational social science, offering a call to action for the scientific community, policymakers, and society. Being positioned at the intersection of AI, data science, and social theory, this paper illuminates the complexities of our digital era and inspires a re-evaluation of the methodologies and ethics guiding our pursuit of knowledge.

The low value of reproductive labor, and the related “crisis of care,” are often attributed to gendered attitudes about work. This article traces this explanation to the attempted synthesis of Marxist and feminist theories of ideology in the 1970s and offers a sympathetic critique with implications for both contemporary theories of labor and the “new ideology critique.” It reconstructs the explanatory role of ideology in feminist analyses of unwaged housework and tracks its uptake in theories of “reproductive labor” more broadly, via what I call the “naturalization thesis.” While these analyses have been influential, I show that they do not provide a convincing account of either gender oppression or the low value of reproductive labor. I offer an alternative explanation for the latter rooted in labor processes and patterns of capital accumulation and argue for the reintegration of ideology critique with the critique of political economy.

Perinatal depression is associated with adverse maternal, newborn and child health outcomes. Treatment gaps and sociocultural factors contribute to its disproportionate burden in low- and middle-income countries (LMICs). Task-sharing approaches, such as peer counseling, have been developed to improve access to mental health services. We conducted a scoping review to map the current literature on peer counseling for perinatal women experiencing depression in LMICs. We searched CINAHL, MEDLINE, APA PsycINFO, Global Health and EMBASE for literature with no date limits. We included 73 records in our analysis, with most being systematic reviews and meta-analyses, randomized controlled trials and qualitative studies. Most studies were conducted in India and Pakistan and published from 2020 onward. The Thinking Healthy Program (THP) and its Peer-Delivered (THPP) adaptation were the most common interventions. Studies suggested effectiveness, feasibility, acceptability and transferability of peer counseling, particularly within the THPP, for perinatal depression. Studies indicated that local women, as peers and lay counselors, are preferred and effective implementation agents. Gaps in the evidence include those relating to understanding perinatal depression (e.g., contextual understandings of the etiology, comorbidity and heterogeneity and social conditions of psychosocial distress including long-term impacts on relationships and children’s development) and understanding and improving implementation. Further research on the adaptation, scaling up and integration of peer-delivered approaches with other approaches to improve impact are needed. There are also gaps in understanding the perspectives and experiences of peer counselors. Evidence gaps may stem from an emphasis on conventional public health approaches and measures derived from Western psychiatry, such as randomized controlled trials. There is relatively little research or implementation that prioritizes peer counselors in terms of understanding their perspectives and experiences (e.g., of professionalization), despite them being central to peer-delivered models. Task sharing has the potential to both empower peer counselors through mental health benefits and professional opportunities but also render peer counselors susceptible to vicarious exposure to traumatic stories and difficult situations amid limitations in available support. Better understanding counselors’ and perinatal women’s experiences can help decolonize the evidence base and improve implementation.

From early on, infants show a preference for infant-directed speech (IDS) over adult-directed speech (ADS), and exposure to IDS has been correlated with language outcome measures such as vocabulary. The present multi-laboratory study explores this issue by investigating whether there is a link between early preference for IDS and later vocabulary size. Infants’ preference for IDS was tested as part of the ManyBabies 1 project, and follow-up CDI data were collected from a subsample of this dataset at 18 and 24 months. A total of 341 (18 months) and 327 (24 months) infants were tested across 21 laboratories. In neither preregistered analyses with North American and UK English, nor exploratory analyses with a larger sample did we find evidence for a relation between IDS preference and later vocabulary. We discuss implications of this finding in light of recent work suggesting that IDS preference measured in the laboratory has low test-retest reliability.

Egalitarian theories assess when and why distributive inequalities are objectionable. How should egalitarians assess inequalities between generations? One egalitarian theory is (telic) distributive egalitarianism: other things being equal, equal distributions of some good are intrinsically better than unequal distributions. I first argue that distributive egalitarianism produces counterintuitive judgements when applied across generations and that attempts to discount or exclude intergenerational inequalities do not work. This being so, intergenerational comparisons also undercut the intragenerational judgements that made distributive egalitarianism intuitive in the first place. I then argue that egalitarians should shed distributive egalitarianism: relational and instrumental arguments against inequality likely suffice to capture egalitarian concerns – including across generations – without encountering the problems produced by distributive egalitarianism.