Power relations and incentive structures in university Public Engagement with urban marginalised actors

There is an expanding body of work examining knowledge coproduction between universities and urban actors (Hemstroem, Palmer, Polk, Perry, & Simon, 2021; Nesti, 2017), reflecting on the pedagogical and social impact of these experiences (Cognetti & Castelnuovo, 2019). This paper will discuss two models of public engagement (PE): the experience of partnering with informal settlement communities and their organisations in setting up a research centre in Sierra Leone (Rigon, Macarthy, Koroma, Walker, & Apsan Frediani, 2017) and the long-term relationship with civil society actors in London and Salvador de Bahia within the framework of an MSc in Social Development Practice. Based on these engagements, the paper will argue for PE to be structurally planned within teaching and research and not as an afterthought. We found that it is only when it is planned as a core activity that it is possible to sustaining it in the long-term and a long-term relationship is fundamental for ethical and impactful PE. The paper will reflect on how planning public engagement at the core of teaching and research transforms the type of knowledge coproduced (Osuteye et al., 2019), and on what role the university and this new type of knowledge can play in relation to urban justice struggles. We found that such knowledge can often provide legitimacy to urban actors and, when used strategically, the university presence can be turned into a powerful tool to get grassroots claims recognised by public institutions. The paper will also reflect on the open-ended and long-term nature of these experimental relations with partners in marginalised urban areas, outlining the challenges and the power issues involved with such knowledge coproduction. The paper will also engage with how the incentive structure of academics shape the public engagement work they do and how academics can find some ‘room for manoeuvre’ for political work within a depoliticised emphasis on ‘impact’ in the neoliberal university. Finally, the paper will consider the incentives and politics of other urban actors for engaging in this work with universities

Intersectional participatory methodologies for climate justice

There is a recognition of how people’s identities across different axes (gender, race/ethnicity, class, age, ability, sexuality, citizenship) determine how they are affected by climate change (Godfrey & Torres, 2016) because these identities shape people’s geography, livelihood, vulnerability and capacity to adapt (Pearse, 2017). Policies and interventions to deal with climate change may further increase inequalities and injustice largely because they are blind to intersectional inequalities, ignore the complex layers of vulnerability, and lack the participation of the affected stakeholders in designing solutions. In this context, there is need for new climate knowledge to counter the disempowering techno-scientific system dominating CC policy (Nightingale et al., 2020). Developing intersectional participatory methodologies to CC helps the move away from extractive knowledge creation towards politicised coproduction approaches able to challenge power structures (McArdle, 2021). An intersectional participatory approach can reveal how these intersectional inequalities interact with CC and policies to address it, transforming power relations at the core of social identities, and lead to more just, democratic, multifaceted and multi-scalar climate solutions (Malin & Ryder, 2018). Building on the author’s work to embed intersectionality in participatory processes, the paper explains the importance of intersectional participatory methods in climate change and present how they can be developed by adapting storytelling, artistic and performative approaches. Such methods can help challenge the anthropocentric lens and consider the interests of non-human animals, the planet and future generations. They can provide fresh insights into complex issues grounded in people’s experience, challenge assumptions, and demonstrate new connections between issues

Participatory Design and Urban Infrastructure - An overview

This brief explores participatory design and planning, its origins and current trends in the context of urban and social infrastructure. It provides a brief overview of the main actors, practitioners’ approaches, and toolkits available, and finally analyses key debates around participatory design of urban social infrastructure

The Governance and Regulation of the Informal Economy: Implications for livelihoods and decent work

A large proportion of workers in the global economy derive their livelihoods from the ‘informal economy’, and these workers are more likely to experience unprotected and exploitative labour conditions. This chapter draws on literature that questions the relevance of a formal/informal dichotomy, and critiques the assumption that extension of state regulation of livelihoods is inherently desirable. Drawing on case study material from Freetown, Sierra Leone, it argues that while state actors play a crucial role in regulation to extend decent work, not all state regulatory practices are beneficial to workers, and that social regulation of livelihoods can also have an important role to play in protecting livelihoods and labour rights. Finally, it highlights the scope of co-production of livelihoods regulation by state and non-state actors

Co-designing built interventions with children affected by displacement (DeCID)

Displacement is at a world record high as a result of persecution, conflict, violence, and human rights violations. Children make up around half of the refugee population worldwide, and 40% of the 80 million displaced people globally. Approximately two-thirds of displaced people live in urban areas, the large majority in developing countries (UNHCR 2020). The quality of spaces available to children has an important impact on child development and wellbeing as it affects a number of children’s rights including play, health, safety and learning. The DeCID handbook was born out of a lack of practical guidelines for co-designing built interventions with children affected by urban displacement. It was created by a mixed team of practitioners and academics from different disciplines, and via a research process involving interviews and thematic discussions with varied related practitioners. This handbook aims to raise the number and quality of built interventions that have been co-designed with children affected by displacement in the urban context: ultimately advancing their wellbeing. It provides practical insights regarding interventions that put children’s wellbeing first, and at the intersection of participatory design, forced displacement, and the urban context

On Hack\u27s Law

Hack\u27s law is reviewed, emphasizing its implications for the elongation of river basins as well as its connections with their fractal characteristics. The relation between Hack\u27s law and the internal structure of river basins is investigated experimentally through digital elevation models. It is found that Hack\u27s exponent, elongation, and some relevant fractal characters are closely related. The self-affine character of basin boundaries is shown to be connected to the power law decay of the probability of total contributing areas at any link and to Hack\u27s law. An explanation for Hack\u27s law is derived from scaling arguments. From the results we suggest that a statistical framework referring to the scaling invariance of the entire basin structure should be used in the interpretation of Hack\u27s law

A MALDI-TOF MS approach for mammalian, human, and formula milks’ profiling

Human milk composition is dynamic, and substitute formulae are intended to mimic its protein content. The purpose of this study was to investigate the potentiality of matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS), followed by multivariate data analyses as a tool to analyze the peptide profiles of mammalian, human, and formula milks. Breast milk samples from women at different lactation stages (2 (n = 5), 30 (n = 6), 60 (n = 5), and 90 (n = 4) days postpartum), and milk from donkeys (n = 4), cows (n = 4), buffaloes (n = 7), goats (n = 4), ewes (n = 5), and camels (n = 2) were collected. Different brands (n = 4) of infant formulae were also analyzed. Protein content (<30 kDa) was analyzed by MS, and data were exported for statistical elaborations. The mass spectra for each milk closely clustered together, whereas different milk samples resulted in well-separated mass spectra. Human samples formed a cluster in which colostrum constituted a well-defined subcluster. None of the milk formulae correlated with animal or human milk, although they were specifically characterized and correlated well with each other. These findings propose MALDI-TOF MS milk profiling as an analytical tool to discriminate, in a blinded way, different milk types. As each formula has a distinct specificity, shifting a baby from one to another formula implies a specific proteomic exposure. These profiles may assist in milk proteomics for easiness of use and minimization of costs, suggesting that the MALDI-TOF MS pipelines may be useful for not only milk adulteration assessments but also for the characterization of banked milk specimens in pediatric clinical settings

Acute effects of a combat sport environment on self-control and pain perception inhibition: a preliminary study in a new ecological framework

Intense sports activities affect cognitive, perceptual, and physiological domains, possibly concurring in reducing pain perception and anxiety. Within a framework for a new socio-ecological narrative that considers opposition (such as fighting) and collaboration (cooperative learning) as carriers of self-control and pain perception inhibition, we investigated the acute effects of a judo-specific session on cognitive (inhibitory control and simple reaction time), perceptual (pain and fatigue), affective (anxiety) and physiological (hormone profile) domains. Eleven male black belt judokas (age 39.0 ± 13.9 years; training experience: 23.2 ± 14.4 years) were recruited and tested before and after judo randori and resting sessions. Inhibitory control and simple reaction time were assessed via the Flanker task and clinical reaction time test, pain and fatigue were assessed using Borg’s category-ratio scale (CR-10), and the state–trait anxiety inventory Y-1 test assessed anxiety. Glucose, insulin, cortisol, creatinine, and irisin levels were measured. Cognitive performance, perception of fatigue and physiological variables increased after randori bouts, while pain decreased. Inhibitory control, perception of pain, perception of fatigue and glucose, cortisol, and creatinine significantly differed (p < 0.05) between randori and resting sessions. A high-intensity randori may induce acute beneficial effects on cognitive, perceptual, and physiological domains. Further studies should compare the results with the outcomes from a collaborative and non-agonist environment and confirm the socio-ecologic framework

Experimental evidence of effective human–AI collaboration in medical decision-making

Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between mds and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human–ai collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an ai support system. Endoscopists were influenced by ai (OR=3.05), but not erratically: they followed the ai advice more when it was correct (OR=3.48) than incorrect (OR=1.85). Endoscopists achieved this outcome through a weighted integration of their and the ai opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human–ai hybrid team to outperform both agents taken alone. We discuss the features of the human–ai interaction that determined this favorable outcome