Implicit Assumption Research Articles

Bewildering the legacy effects of Gail Melson’s wild things/animals/children

ABSTRACT This article bewilders dominant discourses about child-animal relations by acknowledging and challenging the work of Gail Melson who positions animals as providing emotional, social and pedagogical support for children. Melson’s psychological approach rests upon implicit assumptions that shape and support anthropocentrism whilst also critiquing a utilitarian approach to animals in educational learning spaces. The legacy effects of this approach are steeped in neoliberal discourse that entangle with pedagogy and practice. Unless modified these effects pass through generations as sticky webs of knowability that are difficult to contest. Research from Australia and Finland, framed by critical posthuman and relational ontologies, unsettles these effects to reconfigure child-animal relations as fluid and situated. ‘Bewildering education’ grants insights into historical political legacies that can be traced in education policy, practice and theory preoccupied with knowledge development, relations and meaning-making around the productive ‘good’ human subject. Child-animal relations expose complex and far-reaching effects of early childhood because processes of becoming and being human with other animals provides spaces for knowing ‘difference’ as a constituting force that disrupts anthropocentric relations with the world. Building a political history of animals that pays attention to agency and ethical relations reconfigures and reconstitutes animal species, not as objects of pedagogical inquiry, but rather as subjects and fellow earth dwellers.

Informing Structural Assumptions for Three State Oncology Cost-Effectiveness Models through Model Efficiency and Fit.

The characteristics and relative strengths and weaknesses of partitioned survival models (PSMs) and state transition models (STMs) for three state oncology cost-effectiveness models have previously been studied. Despite clear and longstanding economic modeling guidelines, more than one structure is rarely presented, and the choice of structure appears correlated more with audience or precedent than disease, decision problem, or available data. One reason may be a lack of guidance and tools available to readily compare measures of internal validity such as the model fit and efficiency of different structures, or sensitivity of results to those choices. To address this gap, methods are presented to evaluate the fit and efficiency of three structures, with an accompanying R software package, psm3mkv. The methods are illustrated by analyzing interim and final analysis datasets of the KEYNOTE-826 randomized controlled trial. At both interim and final analyses, the STM Clock Reset structure provided the best and most efficient fit. Structural uncertainties had been reduced from interim to final analysis. Beyond measures of internal validity, guidelines highlight the importance of reflecting all available data, avoiding model selection purely on the basis of goodness of fit and strongly considering external validity. The method and software allow modelers to more easily evaluate and report model fit and efficiency, examine implicit assumptions, and reveal sensitivities to structural choices.

Faithful Reasoning over Scientific Claims

Claim verification in scientific domains requires models that faithfully incorporate relevant knowledge from the ever-growing, vast existing literature. Unfaithful claim verifications can lead to misinformation such as those observed during the COVID-19 pandemic. Fact-checking systems often fail to capture the complex relationship between claims and evidence, especially with ambiguous claims and implicit assumptions. Relying only on current LLMs poses challenges due to hallucinations and information traceability issues. To address these challenges, our approach considers multiple viewpoints onto the scientific literature, enabling the assessment of contradictory arguments and implicit assumptions. Our proposed inference method adds faithful reasoning to large language models by distilling information from diverse, relevant scientific abstracts. This method provides a verdict label that can be weighted by the reputation of the scientific articles and an explanation that can be traced back to sources. Our findings demonstrate that humans not only perceive our explanation to be significantly superior to the off-the-shelf model, but they also evaluate it as faithfully enabling the tracing of evidence back to its original sources.

Growth of brown trout in the wild predicted by embryo stress reaction in the laboratory.

Laboratory studies on embryos of salmonids, such as the brown trout (Salmo trutta), have been extensively used to study environmental stress and how responses vary within and between natural populations. These studies are based on the implicit assumption that early life-history traits are relevant for stress tolerance in the wild. Here we test this assumption by combining two data sets from studies on the same 60 families. These families had been experimentally produced from wild breeders to determine, in separate samples, (1) stress tolerances of singly kept embryos in the laboratory and (2) growth of juveniles during 6 months in the wild. We found that growth in the wild was well predicted by the larval size of their full sibs in the laboratory, especially if these siblings had been experimentally exposed to a pathogen. Exposure to the pathogen had not caused elevated mortality among the embryos but induced early hatching. The strength of this stress-induced change of life history was a significant predictor of juvenile growth in the wild: the stronger the response in the laboratory, the slower the growth in the wild. We conclude that embryo performance in controlled environments can be a useful predictor of juvenile performance in the wild.

Indigenous climate justice in Aotearoa New Zealand: the dangers of (mis)recognition within climate policymaking

This paper explores Indigenous climate justice in the settler-colonial context of Aotearoa New Zealand. We adopt the interdisciplinary approach of Critical Discourse Analysis (CDA) to examine the extent to which Māori knowledge systems (mātauranga Māori) and Māori status as Treaty partners with the Crown (New Zealand Government) were recognised included in New Zealand’s Zero Carbon Act (ZCA). Through our exploration we reflect on and explore Māori experiences of climate injustice through lack of recognition or misrecognition. CDA helps to identify whose voices dominated the ZCA process, revealing unequal power relations between stakeholder groups, the Crown, and Māori. By analysing policy documents, long-form submissions from Māori, and interviews with Māori we shed light on the power dynamics which shaped the ZCA process and the implicit assumptions regarding Māori participation in climate policy decision-making. The misrepresentation of Māori as stakeholders rather than treaty partners perpetuates historical legacies of colonization. By applying CDA within the context of climate justice, we highlight tensions between Māori aspirations for partnership and the actual treatment of Māori as stakeholders rather than Treaty partners. By closely examining the narratives surrounding the formation of the ZCA, CDA reveals the ways in which Māori voices are side-lined, their aspirations and concerns dismissed, their Indigenous knowledge systems (mātauranga-a-iwi and mātauranga-a-hapu) marginalized, and their rights to retain their tino rangatiratanga (sovereignty) violated.

The gender minority stress model and/or cisnormativity? The need for pluralistic theoretical perspectives in improving trans health and medicine

The minority stress model has become a well-used framework to explain and analyse health among LGBTQ people, and specifically among transgender and gender-diverse people (TGD), when it is sometimes called ‘the gender minority stress model’. Scholars have argued the need for critical discussions about some of the assumptions underlying the gender minority stress model and how it has been used and discussed. Drawing on a pluralistic understanding of theories and employing a Foucauldian understanding of critique, we discuss implicit assumptions and epistemological standpoints of the gender minority stress model and the connected limitations. We also ask what the concept of cisnormativity can give rise to in comparison with the minority stress framework. We make four arguments: 1) the calls for extensions to the model could be seen as a desire to understand and analyse TGD people's health from an all-encompassing perspective, resulting in theoretical vagueness and the silencing of excluded aspects; 2) in the gender minority stress literature, identity is largely taken for granted and there is no consideration of how power is constitutive for all subjects; 3) the model risks individualising the effects of social norms, and internalisation could be further theoretically developed in relation to the repression hypothesis; 4) in the translation process from LGB minorities to TGD, as well as in thinking about cisnormativity, the issue of gender-affirming care has largely been neglected. By initiating a critical discussion around these issues and illustrating how different theories and frameworks can illuminate different possibilities for thinking and knowing, we aim to open up new routes for thinking about TGD health and medicine.

Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak

Abstract. In late September 2022, explosions of the Nord Stream pipelines caused what could be the largest anthropogenic methane leak ever recorded. We report on Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the sea-foam patch produced by the Nord Stream 2 (NS2) leak located close to Bornholm island, acquired on 29 and 30 September, respectively. Usually, reflected sunlight over sea is insufficient for these Earth imagers to observe any methane signal in nadir-viewing geometry. However, the NS2 foam patch observed here is bright enough to possibly allow the detection of methane above it. We apply the multi-band single-pass (MBSP) method to infer methane enhancement above the NS2 foam patch and then use the integrated mass enhancement (IME) method in a Monte Carlo ensemble approach to estimate methane leak rates and their uncertainties. This very specific NS2 observation case challenges some of MBSP and IME implicit assumptions and thus calls for customized calibrations: (1) for MBSP, we perform an empirical calibration of sea-foam albedo spectral dependence by using sea-foam observations in ship trails, and (2) for IME, we yield a tailored effective wind speed calibration that accounts for a partial plume observation, as methane enhancement may only be seen above the NS2 sea-foam patch. Our comprehensive uncertainty analysis yields large methane leak rate uncertainty ranges that include zero for single overpasses and, assuming they are independent, a best estimate of 502 ± 464 t h−1 for the combined averaged L8 and S-2B emission rate. Within all our Monte Carlo ensembles, positive methane leak rates have higher probabilities (80 %–88 %) than negative ones (12 %–20 %), thus indicating that L8 and S-2B likely captured a methane-related signal. Overall, we see our work both as a nuanced analysis of L8 and S-2B contributions to quantifying the NS2 leak emissions and as a methodological cautionary tale that builds insight into MBSP and IME sensitivities.

‘Refuse Dump, Hurry Up!’: A Cognitive Onomastic and Cultural Metaphor Perspective of Nzema Death-Prevention Names

African personal names have communicative contents that reflect the experiences and expectations of the name-giver as well as the bearer. Death-prevention names, for instance, provide some assurance and security that are vital for a child’s survival, given the implicit assumption that certain spiritual forces are at work. The bestowal of despicable and ‘ugly’ names on children whose preceding siblings died shortly after birth is also a common practice among the Nzema, aiming at preventing succeeding children from death. This study examines cultural conceptions and metaphorical correlations in Nzema death-prevention names. Using 42 death-prevention names obtained through interviews, the study discusses the implications of the names and their metaphoric connections with the objects used to identify this category of people. The study reveals that features of entities such as ɛkpɔtɛ ‘vulture’, nrɛzenra ‘housefly’, kɛndɛne ‘basket’, and fovolɛ ‘refuse dump’ are attributed to these children to make them seem ‘unpleasant’ to the ancestral spirits who are believed to have been snatching them after birth. Other ‘long-lasting’ entities like nyevile ‘sea’, bolɛ ‘rock’, and kpɔma ‘walking stick’ are used metaphorically to refer to a child with the belief that they would survive right from birth and live long on the earth.

Do qubits dream of entangled sheep? Quantum measurement without classical output

Quantum mechanics is usually formulated with an implicit assumption that agents who can observe and interact with the world are external to it and have a classical memory. However, there is no accepted way to define the quantum–classical cut and no a priori reason to rule out fully quantum agents with coherent quantum memories. In this work, we introduce an entirely quantum notion of measurement, called a sensation, to account for quantum agents that experience the world through quantum sensors. Sensations eschew probabilities and instead describe a deterministic flow of quantum information. We quantify the information gain and disturbance of a sensation using concepts from quantum information theory and find that sensations always disturb at least as much as they inform. Viewing measurements as sensations could lead to a new understanding of quantum theory in general and to new results in the context of quantum networks.

Interpretable representations in explainable AI: from theory to practice

Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms. They translate the low-level data representation necessary for good predictive performance into high-level human-intelligible concepts used to convey the explanatory insights. Notably, the explanation type and its cognitive complexity are directly controlled by the interpretable representation, tweaking which allows to target a particular audience and use case. However, many explainers built upon interpretable representations overlook their merit and fall back on default solutions that often carry implicit assumptions, thereby degrading the explanatory power and reliability of such techniques. To address this problem, we study properties of interpretable representations that encode presence and absence of human-comprehensible concepts. We demonstrate how they are operationalised for tabular, image and text data; discuss their assumptions, strengths and weaknesses; identify their core building blocks; and scrutinise their configuration and parameterisation. In particular, this in-depth analysis allows us to pinpoint their explanatory properties, desiderata and scope for (malicious) manipulation in the context of tabular data where a linear model is used to quantify the influence of interpretable concepts on a black-box prediction. Our findings lead to a range of recommendations for designing trustworthy interpretable representations; specifically, the benefits of class-aware (supervised) discretisation of tabular data, e.g., with decision trees, and sensitivity of image interpretable representations to segmentation granularity and occlusion colour.

Relevance of Embodied Practice to Philosophical Understanding

In this paper I argue that meditation has a direct bearing upon philosophical discourse by enabling us to distance ourselves from the basic structure of subjectivity that often limits the scope of reason. Recent neurobiological hypotheses are discussed in conjunction with the method of hermeneutic phenomenology to argue that interpretations on the level of our neurobiology underly and construct our experience of ourselves as subjects and the sense of explicit rational understanding that arises from it. This implies that prediscursive embodied practice can play a crucial role in freeing our philosophical understanding from implicit assumptions.

Limitations of clustering with PCA and correlated noise

It is now common to have a modest to large number of features on individuals with complex diseases. Unsupervised analyses, such as clustering with and without preprocessing by Principle Component Analysis (PCA), is widely used in practice to uncover subgroups in a sample. However, in many modern studies features are often highly correlated and noisy (e.g. SNP's, -omics, quantitative imaging markers, and electronic health record data). The practical performance of clustering approaches in these settings remains unclear. Through extensive simulations and empirical examples applying Gaussian Mixture Models and related clustering methods, we show these approaches (including variants of kmeans, VarSelLCM, HDClassifier, and Fisher-EM) can have very poor performance in many settings. We also show the poor performance is often driven by either an explicit or implicit assumption by the clustering algorithm that high variance features are relevant while lower variance features are irrelevant, called the variance as relevance assumption. We develop practical pre-processing approaches that improve analysis performance in some cases. This work offers practical guidance on the strengths and limitations of unsupervised clustering approaches in modern data analysis applications.

Racial Discrimination in Sourcing: Evidence From Controlled Experiments

Entrepreneurial activity is widely seen as an important tool for addressing the racial wealth gap. However, the survival of minority-owned businesses depends on their ability to win contracts from buyer firms—which might be impacted by buyers’ racial biases. Whether discrimination can exist in sourcing and affect this ability is an unexplored question, perhaps due to an implicit assumption that business-to-business settings are immune from discriminatory biases. In this paper, we use controlled experiments to study whether racial discrimination can affect sourcing decisions. We find that when a supplier's sales manager has a distinctively Black name, buyers are 6.5% less likely (statistically significant at 1% level) to select that supplier compared to a supplier with a sales manager with a distinctively white name. Our findings suggest that equal-opportunity legislation similar to that already in place in the labor market may be needed in the sourcing context. Our findings have implications for executives, suggesting that supplier diversity programs and procurement-bias training can boost corporate performance by expanding the supplier pool, and simultaneously enhance a firm's corporate social responsibility profile.

Understanding careers as translations: the importance of Bruno Latour for the study of careers

PurposeThis paper argues that Bruno Latour’s work on translation provides an alternative to dominant anthropocentric, individualistic and managerial approaches in career studies by considering careers as precarious effects of networks instead of the implicit assumption of individual strategic career actors in extant career research paradigms.Design/methodology/approachThe article first compares the three main current approaches to studying careers – structural functionalist, interpretivist and critical – illustrated by three exemplary empirical studies. Subsequently, three concepts from the sociology of translation that are relevant for the study of careers are introduced: career making as translating interests, careers as effects of networks and career action as dislocated and overtaken. Taken together, these three concepts allow us to conceive of careers as practices performed by human and nonhuman actors. Finally, an example from an ethnographic case study in the field of contemporary art illustrates how a Latourian approach can be used.FindingsLatour’s work on translation provides conceptual and methodological tools to investigate career processes and practices in an era of unpredictability.Originality/valueThe paper introduces Bruno Latour’s work on translation to the study of careers.

Hidden descriptors: Using statistical treatments to generate better descriptor sets

The application of artificial intelligence to chemistry usually focuses on the identification of good correlations between descriptors and a given property of interest. The descriptors often come from arbitrary sets, with the implicit assumption that the evaluation of a sufficiently wide range of descriptors will lead to a satisfactory choice. Recent work in our group has focused on applying statistical analysis to large amounts of DFT results with the goal of finding optimal descriptor sets for a given property, which we label as hidden descriptors. This article briefly discusses this treatment and the chemical knowledge that has been gained through its application in two different domains: metal-ligand bond strength in transition metal complexes, and energy barriers in bimolecular nucleophilic substitution reactions.

The impact of continental shape in 3D geomechanical models of ancient orogenic belts: An example from the Ancestral Rocky Mountain orogeny

The orientations of ancient intra-cratonic uplifts are commonly used to interpret the boundary tectonism style along concealed, modified, or destroyed continental margins. An implicit assumption is that the tectonism-linked stress field strongly correlated with uplift orientations. However, among other uncertainties, the shape of a stressed domain likely has an influence upon the interior stress field. In many cases of pre-Cenozoic orogenesis, multiple feasible restorations of a continent margin exist. To explore the influence of continent margin position and shape on a hypothetical ancient continental stress field we developed a new 3D geomechanical finite element method (FEM) to evaluate scenarios for the late Paleozoic Ancestral Rocky Mountain orogen (ARMO). Tectonic scenarios were calculated across a range of domains constrained by geologic uncertainty in the pre-ARMO geometry of the continent. Uncertainty for continent geometry prior to the ARMO is estimated from several published palinspastic restorations. The results demonstrate that 10s–100s of km of uncertainty in margin position, as is the case of the ARMO, should not prevent the linkage of stress patterns to styles of boundary tectonism. This work shows it is plausible to simplify the analysis of the ARMO stress/strain field utilizing one continental geometry.

Principles for Adapting Assessments of Executive Function across Cultural Contexts.

Direct assessments of executive functions (EFs) are increasingly used in research and clinical settings, with a central assumption that they assess "universal" underlying skills. Their use is spreading globally, raising questions about the cultural appropriateness of assessments devised in Western industrialized countries. We selectively reviewed multidisciplinary evidence and theory to identify sets of cultural preferences that may be at odds with the implicit assumptions of EF assessments. These preferences relate to motivation and compliance; cultural expectations for interpersonal engagement; contextualized vs. academic thinking; cultural notions of speed and time; the willingness to be silly, be incorrect, or do the opposite; and subject-matter familiarity. In each case, we discuss how the cultural preference may be incompatible with the assumptions of assessments, and how future research and practice can address the issue. Many of the cultural preferences discussed differ between interdependent and independent cultures and between schooled and unschooled populations. Adapting testing protocols to these cultural preferences in different contexts will be important for expanding our scientific understanding of EF from the narrow slice of the human population that has participated in the research to date.

Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity

AbstractRecent models of the unsaturated hydraulic conductivity curve (UHCC) are the sum of separate UHCCs for domains of capillary water, film water, and water vapor. This requires parallel, noninteracting domains. A theoretical framework for aggregating domain conductivities to a bulk soil UHCC is presented to identify and possibly relax implicit assumptions about domain configuration. The paper develops arithmetic, harmonic, and geometric averages of the liquid‐water conductivities that can be arithmetically averaged with the vapor conductivity. However, current models for capillary and film conductivities are intrinsic, that is, valid within their respective domain. The vapor conductivity is a bulk conductivity, that is, it gives the conductivity of the gaseous domain as it manifests itself in the soil. Conversion relationships use the domain volume fractions as approximations of the as‐yet unknown weighting factors to convert between intrinsic and bulk conductivities. This facilitates consistent averaging of domain conductivities. Even with consistent averaging, a truly physically accurate model of the UHCC based on domain conductivities is fundamentally elusive. Nevertheless, models based on the three averages and the unweighted sum of the domain conductivities produce good fits to data for two soils but diverge in the dry range. The fitted curves for the capillary and film water depend on the averaging (or adding) method. Hence, they are not strictly characteristic of their respective domains. The true intrinsic domain conductivity functions may be impossible to determine.

Validity constraints for data analysis workflows

Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming.We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behavior (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.

Demystifying Algorithmic Fairness in an Uncertain World

Significant progress in the field of fair machine learning (ML) has been made to counteract algorithmic discrimination against marginalized groups. However, fairness remains an active research area that is far from settled. One key bottleneck is the implicit assumption that environments, where ML is developed and deployed, are certain and reliable. In a world that is characterized by volatility, uncertainty, complexity, and ambiguity, whether what has been developed in algorithmic fairness can still serve its purpose is far from obvious. In this talk, I will first discuss how to improve algorithmic fairness under two kinds of predictive uncertainties, i.e., aleatoric uncertainty (i.e., randomness and ambiguity in the data) and epistemic uncertainty (i.e., a lack of data or knowledge), respectively. The former regards historical bias reflected in the data and the latter corresponds to the bias perpetuated or amplified during model training due to lack of data or knowledge. In particular, the first work studies pushing the fairness-utility trade-off through aleatoric uncertainty, and the second work investigates fair few-shot learning. The last work introduces coverage-based fairness that ensures different groups enjoy identical treatment and receive equal coverage.