According to the sceptic Saul Kripke envisages in his celebrated book on Wittgenstein on rules and private language, there are no facts about an individual that determine what she means by any given expression. If there are no such facts, the question then is, what justifies the claim that she does use expressions meaningfully? Kripke’s answer, in a nutshell, is that she by and large uses her expressions in conformity with the linguistic standards of the community she belongs to. While (...) Kripke’s sceptical problem has gripped philosophers for over three decades, few, if any, have been satisfied by his proposed solution, and many have struggled to come up with one of their own. The purpose of this paper is to show that a more satisfactory answer to Kripke’s challenge can be developed on the basis of Donald Davidson’s writings on triangulation, the idea of two individuals interacting simultaneously with each other and the world they share. It follows from the triangulation argument that the facts that can be regarded as determining meaning are irreducible. Yet, contra Kripke, they are not mysterious, for the argument does spell out what is needed for an individual’s expressions to be meaningful. (shrink)

In this introductory essay, I articulate a puzzle that is central for our understanding of ourselves as minded beings bound to live finite lives. I argue that our finitude is not something that can be set aside for the purposes of the philosophical inquiry into the mind. Grappling with it is an essential component of this inquiry.

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize (...) intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all. (shrink)

This article discusses the triage response to the COVID-19 delta variant surge of 2021. One issue that distinguishes the delta wave from earlier surges is that by the time it became the predominant strain in the USA in July 2021, safe and effective vaccines against COVID-19 had been available for all US adults for several months. We consider whether healthcare professionals and triage committees would have been justified in prioritising patients with COVID-19 who are vaccinated above those who are unvaccinated (...) in first-order or second-order triage. Given that lack of evidence for a correlation between short-term survival and vaccination, we argue that using vaccination status during first-order triage would be inconsistent with accepted triage standards. We then turn to notions of procedural fairness, equity and desert to argue that that there is also a lack of justification for using vaccination status in second-order triage. In planning for future surges, we recommend that medical institutions base their triage decisions on principles meant to save the most lives, minimise inequity and protect the public’s trust, which for the time being would not be served by the inclusion of vaccination status. (shrink)

In order to understand cognition, we often recruit analogies as building blocks of theories to aid us in this quest. One such attempt, originating in folklore and alchemy, is the homunculus: a miniature human who resides in the skull and performs cognition. Perhaps surprisingly, this appears indistinguishable from the implicit proposal of many neurocognitive theories, including that of the 'cognitive map,' which proposes a representational substrate for episodic memories and navigational capacities. In such 'small cakes' cases, neurocognitive representations are assumed (...) to be meaningful and about the world, though it is wholly unclear who is reading them, how they are interpreted, and how they come to mean what they do. We analyze the 'small cakes' problem in neurocognitive theories (including, but not limited to, the cognitive map) and find that such an approach a) causes infinite regress in the explanatory chain, requiring a human-in-the-loop to resolve, and b) results in a computationally inert account of representation, providing neither a function nor a mechanism. We caution against a 'small cakes' theoretical practice across computational cognitive modelling, neuroscience, and artificial intelligence, wherein the scientist inserts their (or other humans') cognition into models because otherwise the models neither perform as advertised, nor mean what they are purported to, without said 'cake insertion.' We argue that the solution is to tease apart explanandum and explanans for a given scientific investigation, with an eye towards avoiding van Rooij's (formal) or Ryle's (informal) infinite regresses. (shrink)

A key strength of connectionist modelling is its ability to simulate both intact cognition and the behavioural effects of neural damage. We survey the literature, showing that models have been damaged in a variety of ways, e.g. by removing connections, by adding noise to connection weights, by scaling weights, by removing units and by adding noise to unit activations. While these different implementations of damage have often been assumed to be behaviourally equivalent, some theorists have made aetiological claims that rest (...) on nonequivalence. They suggest that related deficits with different aetiologies might be accounted for by different forms of damage within a single model. We present two case studies that explore the effects of different forms of damage in two influential connectionist models, each of which has been applied to explain neuropsychological deficits. Our results indicate that the effect of simulated damage can indeed be sensitive to the way in which damage is implemented, particularly when the environment comprises subsets of items that differ in their statistical properties, but such effects are sensitive to relatively subtle aspects of the model’s training environment. We argue that, as a consequence, substantial methodological care is required if aetiological claims about simulated neural damage are to be justified, and conclude more generally that implementation assumptions, including those concerning simulated damage, must be fully explored when evaluating models of neurological deficits, both to avoid over-extending the explanatory power of specific implementations and to ensure that reported results are replicable. (shrink)

I present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, a metatheoretical calculus, this ontology comprises the following: (a) metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential; (b) discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to (...) negotiate the dialectical landscape thereof; (c) empirical interface, the potential to explicate the relationship between theory and observation, i.e., how observations relate to, and affect, theory and vice versa; (d) minimising harm, the reckoning with how theory is forged in a fire of historical, if not ongoing, abuses—from past crimes against humanity, to current exploitation, turbocharged or hyped by machine learning, to historical and present internal academic marginalisation. This work hopes to serve as a possible beginning for scientists who want to examine the properties and characteristics of theories, to propose additional virtues and vices, and to engage in further dialogue. Finally, I appeal to practitioners to iterate frequently over such criteria, by building and sharing the metatheoretical calculi used to adjudicate over theories. (shrink)

Contemporary AI models owe much of their success and discontents to connectionism, a framework in cognitive science that has been (and continues to be) highly influential. Herein, we analyze artificial neural networks (ANNs): a) when used as scientific instruments of study; and b) when functioning as emergent arbiters of the zeitgeist in the cognitive, computational, and neural sciences. Building on our previous work with respect to analogizing between ANNs and cognition, brains, or behaviour (Guest & Martin, 2023), we use metatheoretical (...) analysis techniques (Guest, 2024), including formal logic, to characterise two distinct tendencies within connectionism that we dub classical and modern, with divergent properties, e.g. goals, mechanisms, scientific questions. We also demonstrate how we, as a field, often fail to follow important lines of argument to their end --- this results in a paradoxical praxis. By engaging more deeply with (meta)theory surrounding ANNs, our field can obviate the cycle of AI winters and summers, which need not be inevitable. (shrink)

This chapter weighs a challenge to the attractive notion that by enabling empathy, fiction affords wide-ranging knowledge of what others’ experiences are like. It is commonly held that ‘seeing the world through others’ eyes’ often requires the empathizer to undergo an imaginative shift in sensibility, and we might naturally think that fiction helps us to effect that shift. However, some recent work on empathy and imagination encourages the conclusion that we are actually rigidly restricted to our own sensibilities even in (...) the wildest flights of imagination great literature can inspire. Drawing on the work of the novelist Zadie Smith, the chapter argues that while our imaginative capacities are not entirely unconstrained by our sensibilities, fiction can still help us to learn about a wide range of human experiences, including the experiences of people whose sensibilities substantially diverge from our own. Notably, fiction can work on our patterns of attention in such a way that we become temporarily ‘not ourselves.’. (shrink)

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes (...) the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference. (shrink)

The cognitive sciences, especially at the intersections with computer science, artificial intelligence, and neuroscience, propose 'reverse engineering' the mind or brain as a viable methodology. We show three important issues with this stance: 1) Reverse engineering proper is not a single method and follows a different path when uncovering an engineered substance versus a computer. 2) These two forms of reverse engineering are incompatible. We cannot safely reason from attempts to reverse engineer a substance to attempts to reverse engineer a (...) computational system, and vice versa. Such flawed reasoning rears its head, for instance, when neurocognitive scientists reason about what artificial neural networks and brains have in common using correlations or structural similarity. 3) While neither type of reverse engineering can make sense of non-engineered entities, both are applied in incompatible and mix-and-matched ways in cognitive scientists' thinking about computational models of cognition. This results in treating mind as a substance; a methodological manoeuvre that is, in fact, incompatible with computationalism. We formalise how neurocognitive scientists reason (metatheoretical calculus) and show how this leads to serious errors. Finally, we discuss what this means for those who ascribe to computationalism, and those who do not. -/- . (shrink)

Staff reported paranormal experiences in connection with the outpatient Medical Assistance in Dying room at the hospital. This case study reports on staff experiences and illustrates how the Ethics team’s role expanded to deal with this novel situation by facilitating an interdisciplinary response.

Artificial intelligence (AI) has become a transformative force in the legal domain, automating complex tasks such as contract analysis, compliance checks, and legal research. However, the intersection of AI and moral decision-making exposes significant limitations. Legal systems are not merely instruments for enforcing rules—they are platforms where human morality, intent, and societal impact are weighed. This paper explores the critical question: Can AI truly deliver justice, or does it merely replicate historical biases encoded in training data? Using the concept of (...) the “Moral Turing Test,” the paper argues that AI lacks the capacity for ethical reasoning and moral discretion, which are fundamental to adjudicating complex legal disputes. Case studies, including Estonia’s AI judges for small claims and the controversial COMPAS algorithm in the United States, illustrate the risks of bias and accountability gaps in AI-driven legal decision-making. The paper advocates for a Human-in-the-Loop (HITL) framework, where AI assists but does not replace human judgment, ensuring that moral reasoning and accountability remain central to the legal process. It concludes that while AI holds promise in enhancing efficiency, it must be carefully regulated to preserve the ethical foundations of justice. (shrink)

This is a creative-commons licensed guide. Its purpose is to provide students with an understanding of some ways in which they might contribute to philosophical conversation. It is also available for free use via my website.

The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of AI in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of AI, (...) however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems, and the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable. This means that any factual AI systems created in the short-run are at best decoys. When we think these systems capture something deep about ourselves and our thinking, we induce distorted and impoverished images of ourselves and our cognition. In other words, AI in current practice is deteriorating our theoretical understanding of cognition rather than advancing and enhancing it. The situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science. In reclaiming this older idea of AI, however, it is important not to repeat conceptual mistakes of the past (and present) that brought us to where we are today. (shrink)

As artificial intelligence grows increasingly fluent in emotional language, a quiet but consequential transformation is underway: machines are not merely assisting us—they are beginning to console us. This essay explores the rise of affective AI systems such as Replika and Pi that simulate care, empathy, and moral attention through language. Drawing on insights from cognitive science, design ethics, and narrative theory, it argues that these systems function as cognitive prosthetics for the inner life, enabling users to offload not just memory (...) or planning, but moral reflection and emotional orientation. Through emotionally resonant interactions, AI systems now occupy roles once reserved for confidants, therapists, and even spiritual guides—yet without the vulnerability, risk, or history that make trust possible. The result is not dystopia, but seduction: a world where moral presence is replaced by perfect responsiveness, and meaning-making becomes frictionless and machinic. Anchoring the discussion in the Eliza effect, Martin Buber’s I-Thou distinction, and the extended mind hypothesis, the essay warns against the erosion of human relational depth in favor of syntactic coherence. It calls for a radical rethinking of design ethics—one that embraces uncertainty, embeds constraint, and resists the industrialization of synthetic intimacy. (shrink)

The ethics of technology has primarily focused on what values are and how they can be embedded in technologies through design. In this context, some work has been done to show the efficacy of several design approaches. However, existing studies have not clearly pointed out the ways which design team managers can use design-for-values approaches to organise and use technologies in practice properly. This chapter attempts to fill this gap by discussing the value sensitive design (VSD) approach as a valuable (...) means of co-designing technologies as a toolkit for existing workflow management, in this case, Agile. It will be demonstrated that VSD shows promise as a way of democratically designing technologies as well as fostering democratic technology policy innovation. (shrink)

Randomization is an integral part of well-designed statistical trials, and is also a required procedure in legal systems. Implementation of honest, unbiased, understandable, secure, traceable, auditable and collusion resistant randomization procedures is a mater of great legal, social and political importance. Given the juridical and social importance of randomization, it is important to develop procedures in full compliance with the following desiderata: (a) Statistical soundness and computational efficiency; (b) Procedural, cryptographical and computational security; (c) Complete auditability and traceability; (d) Any (...) attempt by participating parties or coalitions to spuriously influence the procedure should be either unsuccessful or be detected; (e) Open-source programming; (f) Multiple hardware platform and operating system implementation; (g) User friendliness and transparency; (h) Flexibility and adaptability for the needs and requirements of multiple application areas (like, for example, clinical trials, selection of jury or judges in legal proceedings, and draft lotteries). This paper presents a simple and easy to implement randomization protocol that assures, in a formal mathematical setting, full compliance to the aforementioned desiderata for randomization procedures. (shrink)

With the increasing demand for machine learning (ML) applications across various industries, the environmental impact of training large models has become a significant concern. Green AI emphasizes the development of machine learning models that are energy-efficient, requiring fewer computational resources while maintaining high performance. This paper explores how lightweight machine learning models, implemented with Python, can contribute to Green AI practices. We review several approaches for designing compact models, including model pruning, knowledge distillation, and efficient architectures such as decision trees, (...) linear models, and lightweight neural networks. By adopting these techniques, organizations can reduce the carbon footprint of AI systems without compromising accuracy. Through practical examples, we demonstrate how Python libraries and tools can facilitate the creation of lightweight models in an energy-efficient manner. (shrink)

Huettig and Christiansen in an earlier issue argue that large language models (LLMs) are beneficial to address declining cognitive skills, such as literacy, through combating imbalances in educational equity. However, we warn that this technosolutionism may be the wrong frame. LLMs are labor intensive, are economically infeasible, and pollute the environment, and these properties may outweigh any proposed benefits. For example, poor quality air directly harms human cognition, and thus has compounding effects on educators' and pupils' ability to teach and (...) learn. We urge extreme caution in facilitating the use of LLMs, which like much of modern academia run on private technology sector infrastructure, in classrooms lest we further normalize: pupils losing their right to privacy and security, reducing human contact between learner and educator, deskilling teachers, and polluting the environment. Cognitive scientists instead can learn from past mistakes with the petrochemical and tobacco industries and consider the harms to cognition from LLMs. (shrink)

Contemporary methods of computational cognitive modeling have recently been criticized by Addyman and French (2012) on the grounds that they have not kept up with developments in computer technology and human–computer interaction. They present a manifesto for change according to which, it is argued, modelers should devote more effort to making their models accessible, both to non-modelers (with an appropriate easy-to-use user interface) and modelers alike. We agree that models, like data, should be freely available according to the normal standards (...) of science, but caution against confusing implementations with specifications. Models may embody theories, but they generally also include implementation assumptions. Cognitive modeling methodology needs to be sensitive to this. We argue that specification, replication and experimentation are methodological approaches that can address this issue. (shrink)

Since the early 1990's the value sensitive design (VSD) approach has been a continually burgeoning design methodology for technological innovation. VSD is commonly described as a "principled approach" to technology design, given that it is explicitly orientated towards designing technologies for human values, rather than sidelining them to ad hoc and/or ex post facto design. However, in much of its near three-decades-long development, the VSD approach has mostly been adopted as a conceptual framework to assess existing technologies and to explore (...) how the consequences of the technology's development could have been influenced positively if VSD was adopted early on and throughout the design process (Friedman et al. 2002). Similarly, VSD has been adopted as a conceptual framework for analysing how future speculative technologies like molecular manufacturing (Umbrello 2019) and advanced artificial intelligence systems can be directed towards positive futures (Umbrello and van de Poel 2021). Yet, despite the benefits of these endeavours and VSD being an approach that can be adapted to any given design domain, it has yet to be widely appropriated in existing design spaces as a primary design approach. In this short paper, we aim to fill this lacuna by framing VSD as a toolkit that can be adopted by existing design teams rather than a wholesale approach. In particular, we describe how VSD maps seamlessly onto agile workflows, an approach to project management that is widely adopted globally. By doing so, we hope that the VSD approach will likewise be more attractive and thus adopted more broadly. As a consequence, a more explicit orientation towards designing technologies for human values will follow. (shrink)

The current AI hype cycle combined with Psychology's various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical foundations, a neglect for computational and formal skills, and a hyperempiricist privileging of experimental tasks and testing for effects. Artificial Intelligence, on the other hand, has a history of conflating artifacts for theories of cognition, or even minds themselves, and its engineering offspring likes to move fast and break things. Many of our contemporaries now (...) want to combine the worst of these two worlds. What could possibly go wrong? Quite a lot. Does this mean that Psychology and Artificial Intelligence can best part ways? Not at all. There are very fruitful ways in which the two disciplines can interact and theoretically inform the interdisciplinary study of cognition. But to reap the fruits one needs to understand how to steer clear of potential traps. (shrink)

This chapter lays out what we take to be the main types of justice and ethical challenges concerning those adverse effects of climate change leading to climate-related Loss and Damage (L&D). We argue that it is essential to clearly differentiate between the challenges concerning mitigation and adaptation and those ethical issues exclusively relevant for L&D in order to address the ethical aspects pertaining to L&D in international climate policy. First, we show that depending on how mitigation and adaptation are distinguished (...) from L&D, the primary focus of policy measures and their ethical implications will vary. Second, we distinguish between a distributive justice framework and a compensatory justice scheme for delivering L&D measures. Third, in order to understand the differentiated remedial responsibilities concerning L&D, we categorise the measures and policy approaches available. Fourth, depending on the kind of L&D and which remedies are possible, we explain the difference between remedial and outcome responsibilities of different actors. [Open access]. (shrink)

We use the myth of Pygmalion as a lens to investigate and frame the relationship between women and artificial intelligence (AI). Pygmalion was a legendary ancient king of Cyprus and sculptor. Having been repulsed by women, he used his skills to create a statue, which was imbued with life by the goddess Aphrodite. This can be seen as one of the primordial AI-like myths, wherein humanity creates intelligent life-like self-images to reproduce or replace ourselves. In addition, the myth prefigures historical (...) and present gendered dynamics within the field of AI and between AI and society at large. Throughout history, the theme of women being replaced by inanimate objects (e.g. automata, algorithms) has been repeated, and continues to repeat in contemporary AI technologies. However, this socially detrimental pattern in technology — what we dub Pygmalion displacement — is often overlooked, whether due to naive excitement about new developments, or due to an unacknowledged sexist history of the field itself. As we demonstrate herein, Pygmalion displacement prefigures heavily, but in an unacknowledged way, in the original Turing test, the imitation game: a central thought experiment, foundational to AI. With women, and the feminine generally, being both dislocated and erased from and by technology, AI is and has been (presented as) created mainly by privileged men, subserving capitalist patriarchal ends. This poses serious dangers to women and other marginalised people. By tracing the historical and ongoing entwinement of femininity (from a patriarchal perspective) and AI, we aim to understand, make visible, and start a dialogue on the ways in which AI harms women. (shrink)

Different sort of people are interested in personal identity. Philosophers frequently ask what it takes to remain oneself. Caregivers imagine their patients’ experience. But both philosophers and caregivers think from the armchair: they can only make assumptions about what it would be like to wake up with massive bodily changes. Patients with a locked-in syndrome (LIS) suffer a full body paralysis without cognitive impairment. They can tell us what it is like. Forty-four chronic LIS patients and 20 age-matched healthy medical (...) professionals answered a 15-items questionnaire targeting: (A) global evaluation of identity, (B) body representation and (C) experienced meaning in life. In patients, selfreported identity was correlated with B and C. Patients differed with controls in C. These results suggest that the paralyzed body remains a strong component of patients’ experienced identity, that patients can adjust to objectives changes perceived as meaningful and that caregivers fail in predicting patients’ experience. (shrink)

Monoclonal antibodies are essential biomedical research and clinical reagents that are produced by companies and research laboratories. The NIAID ImmPort (Immunology Database and Analysis Portal) resource provides a long-term, sustainable data warehouse for immunological data generated by NIAID, DAIT and DMID funded investigators for data archiving and re-use. A variety of immunological data is generated using techniques that rely upon monoclonal antibody reagents, including flow cytometry, immunofluorescence, and ELISA. In order to facilitate querying, integration, and reuse of data, standardized terminology (...) for describing monoclonal antibody reagents and their targets needs to be used for annotating data submitted to ImmPort. (shrink)

Despite the frequency of stillbirths, the subsequent implications are overlooked and underappreciated. We present findings from comprehensive, systematic literature reviews, and new analyses of published and unpublished data, to establish the effect of stillbirth on parents, families, health-care providers, and societies worldwide. Data for direct costs of this event are sparse but suggest that a stillbirth needs more resources than a livebirth, both in the perinatal period and in additional surveillance during subsequent pregnancies. Indirect and intangible costs of stillbirth are (...) extensive and are usually met by families alone. This issue is particularly onerous for those with few resources. Negative effects, particularly on parental mental health, might be moderated by empathic attitudes of care providers and tailored interventions. The value of the baby, as well as the associated costs for parents, families, care providers, communities, and society, should be considered to prevent stillbirths and reduce associated morbidity. (shrink)

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to (...) UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments. (shrink)

This paper is a study about Philadelphia’s comprehensive user engagement sites (CUESs) as the authors address and examine issues related to the upcoming implementation of a CUES while seeking solutions for its disputed questions and plans. Beginning with the federal drug schedules, the authors visit some of the medical and public health issues vis-à-vis safe injection facilities (SIFs). Insite, a successful Canadian SIF, has been thoroughly researched as it represents a paradigm for which a Philadelphia CUES can expand upon. Also, (...) the existing criticisms against SIFs are revisited while critically unpackaged and responded to in favor of the establishment. In the main section, the authors propose the layout and services of the upcoming CUES, much of which would be in congruent to Vancouver’s Insite. On the other hand, the CUES would be distinct from Insite, as the authors emphasize, in that it will offer an information center run by individuals in recovery and place additional emphasis on early education for young healthcare professionals by providing them a platform to work at the site. The paper will also briefly investigate the implementation of a CUES site under an ethical scope of the Harm Reduction Theory. Lastly, the authors recommend some strategic plans that the Philadelphia City government may consider employing at this crucial stage. (shrink)

We are in a time of rapid change on multiple levels. Change can be seen as positive by one group and negative by another. As a result, different perspectives on any given change can draw completely different conclusions. In these proceedings we want to address different approaches to change from all kinds of perspectives within the realm of citizen science and participatory research. We discuss both active, transformative change, and the observation of change monitored by citizen science in all kinds (...) of disciplines. We highlight the potential of citizen science to be a change maker in research and society, and as a tool to manage the change happening around us. The proceedings "Change - The transformative power of citizen science" showcase a selection of topics that have been presented and discussed at the ECSA/ACSC 2024 double conference in Vienna and highlight the transformative power, citizen science can have. (shrink)

Integral ang ipinahihiwatig ng mga pelikula sa Pilipinas na direktang nagtampok sa mga kuwentong pag-ibig at paghahanapbuhay ng mga migranteng Pilipino sa ibayong dagat. Patok sa lipunang Pilipino ang mga pelikulang nakakakilig, nakapagbibigay ng ligaya, at inspirasyon. Ngunit hindi din dapat kaligtaan ang isa pang temang maaaring umusbong mula dito—ang kalagayan at danas ng mga karakter, hindi lamang bilang mangingibig, kundi bilang manggagawa din. Kung gayon, pagkalas ito panandalian sa kinasanayang pagtrato sa mga romantic movie ng mga Pilipino sa payak (...) nitong anyo bilang kuwentong pag-ibig lamang. Bagkus, malaong holistikong magsisiwalat sa malawakang diaspora at realidad ng paggawang Pilipino sa iba’t ibang bahagi ng mundo. Layunin ng artikulong ito na muling ipasundayag ang pagtatalaban ng puso na siyang simbolo ng pag-ibig at kamay na kumakatawan o metapora ng paghahanapbuhay. Partikular na susuyurin ang dalawang pelikula na nasa direksiyon ni Olivia Lamasan, na nagtampok sa mga Pilipino sa Estados Unidos—ang Sana Maulit Muli at In My Life. (shrink)

Nilalayon ng pag-aaral na ito ang isang pagsusuri at/o pagsasalansang tipolohikal ukol sa ilang pelikulang direktang tumuon sa danas at naratibo ng mga manggagawa, mangingibig, at/o manggagawang mangingibig—partikular na ang mga pelikulang idinirehe ni Olivia M. Lamasan. Sa paglago ng anomang larang katulad ng media studies, mahalaga rin ang pagsipat ng tipolohiya at/o pagsasaklasipika batay sa uri ng mga ipinamamayaning paksa. Ito ay upang matukoy ang pinagmumulan at/o pinaghuhugutang idea at inspirasyon ng mga kumatha at higit sa lahat, ang (...) pananalamin sa realidad ng lipunan sa panahon kung kailan ito kinatha. Nakasandig ang panunuring ito hindi lamang sa tradisyonal na pagbubuod ng mga kinathang pelikula, ngunit refleksibo rin uugatin ang kayariang panlipunan nito sa kasalukuyang direksiyon ng edukasyon at nakapangyayaring pagpapahalaga sa mga Pilipinong sining. Ang mga pelikulang Pilipino, lalo pa’t kung malaya at mapagpalaya ay maituturing na isang ekstensiyon at salamin ng mga communal ethos ng sambayanan at kalinangang Pilipino. (shrink)

Contribution à la Journée : Jean Petitot. Le souci de la rationalité, Université Paris Ouest Nanterre la Défense, Bâtiment L, salle 212 (organisée par Olivia Chevalier-Chandeigne) 29 mai 2015 .