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BACKGROUND: It has been shown that a recently defined stressor, 'illegitimate tasks', has negative effects on employees' work motivation and health. Better understanding of the illegitimate tasks undertaken by physicians might contribute to a more resource-efficient division of labour within the health care system, with beneficial effects on organisational economics and employee performance. We aimed to investigate the prevalence of unreasonable illegitimate tasks, their associations with workplace variables and their impact on health, in particular sickness presenteeism. METHODS: Cross-sectional data were collected in 2012. A sample of 545 Norwegian physicians answered an online questionnaire. The response rate was high (71.8%). The data were analysed using independent-samples t-tests, ANOVA and logistic regression. RESULTS: About 50.2% of physicians in all clinical positions reported that at least 11% of their everyday tasks could have been done by other hospital personnel. Seven percent of the physicians reported that at least 31% of their daily workload consisted of unreasonable illegitimate tasks. There were no significant differences in unreasonable illegitimate tasks according to clinical position, age or gender. Administrative task load and role conflict were positively associated with unreasonable illegitimate tasks that physicians reported could be reallocated to non-medical professionals. Moreover, unreasonable illegitimate task was associated with a higher probability of sickness presenteeism after controlling for age, gender, role conflict, control over work pace, exhaustion and administrative tasks. CONCLUSIONS: The results confirm that physicians' workload includes a high proportion of unreasonable illegitimate tasks and that this can contribute to sickness presenteeism. Investigation of work environmental factors can provide insight into the mechanisms behind unreasonable illegitimate tasks. Decreasing the amount of administrative tasks and role conflicts faced by physicians should be a priority. These findings could be used to make hospital task management more resource-efficient. Our results indicate that a substantial proportion of physicians' work capacity could be re-allocated to core tasks. Further research is needed into the specific type and content of unreasonable illegitimate tasks undertaken by physicians in order to determine to whom they should be allocated to ensure a healthy and motivated workforce and provision of high quality, resource-efficient health care services.

Renowned playwright George Bernard Shaw once said reasonable man adapts himself to the world, the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man. By this definition, some of today's entrepreneurs are decidedly unreasonable--and have even been dubbed crazy. Yet as John Elkington and Pamela Hartigan argue in The Power of Unreasonable People, our very future may hinge on their work. Through vivid stories, the authors identify the highly unconventional entrepreneurs who are solving some of the world's most pressing economic, social, and environmental problems. They also show how these pioneers are disrupting existing industries, value chains, and business models--and in the process creating fast-growing markets around the world. By understanding these entrepreneurs' mindsets and strategies, you gain vital insights into future market opportunities for your own organization. Providing a first-hand, on-the-ground look at a new breed of entrepreneur, this book reveals how apparently unreasonable innovators have built their enterprises, how their work will shape risks and opportunities in the coming years, and what tomorrow's leaders can learn from them. Start investing in, partnering with, and learning from these world-shaping change agents, and you position yourself to not only survive but also thrive in the new business landscape they're helping to define.

In this study, we claim that political liberalism, despite harsh criticism, is still the best option available for providing a just and stable society. However, we maintain that political liberalism needs to be revised so as to be justifiable from the perspective of not only the “reasonable” in a Rawlsian sense (that we define as “fully” reasonable) but also the ones whom Rawls labels as “unreasonable.” To support our claim, going beyond Rawls’s original account, we unpack the concept of unreasonableness and identify three different subsets that we label as the “partially reasonable,” the “non-reasonable,” and the “unreasonable.” We argue that both the “fully” reasonable and the “partially reasonable” would be included into the constituency of public justification; more specifically, we claim that the latter would support liberal institutions out of their reasons: we define these reasons as mutually intelligible reasons and claim that they allow to acknowledge the importance of a convergence approach to public justification. As for the “non-reasonable” and “unreasonable,” we claim that they cannot be included in the constituency of public justification, but they nonetheless could be compliant with liberal institutions if political liberalism offers them some reasons to comply: here, we claim that political liberalism should include them through engagement and propose reasoning from conjecture as an effecting way of offering reasons for compliance. In particular, we claim that through reasoning from conjecture, the “non-reasonable” could find conciliatory reasons to comply with liberal institutions on a stable base. With regard to the “unreasonable” in the strict sense, we claim that through reasoning from conjecture, their unreasonableness could be contained and they could find reasons—even if just self-interested—for complying with liberal institutions rather than defying them. In our discussion, we consider the different subsets not as “frozen” but as dynamic and open to change, and we aim to propose a more complex and multilayered approach to inclusion that would be able to include a wider set of people. To strengthen our argument, we show that the need for a wider public justification and for broader inclusion in liberal societies is grounded in respect for persons both as equal persons and as particular individuals. In particular, we claim that individuals’ values, ends, commitments, and affiliations activate demands of respect and can strengthen the commitment to the liberal–democratic order. Through a reformulation of the role of respect in liberal societies, we also show a kind of social and communitarian dimension that, we claim, is fully compatible with political liberalism and opens it up to “civic friendship” and “social solidarity,” which are constitutive elements for the development of a sense of justice and for the realization of a just and stable society.

OBJECTIVE: This article was written to argue that physicians are not ethically obligated to provide care which they consider futile, unreasonable, or both, either voluntarily or in response to patient or surrogate demands. DATA SOURCES: Data used to prepare this article were drawn from published articles, including original investigations, position papers and editorials in the author's personal files. STUDY SELECTION: Articles were selected for their relevance to the subjects of medical ethics, the concepts of futility and medical reasonableness, case law, and healthcare reform. DATA EXTRACTION: The author extracted all applicable data. DATA SYNTHESIS: Physicians may feel obligated to provide care in all clinical circumstances due to the single master view of medicine and the ethical principle of autonomy. However, care may be considered futile according to several definitions of that word, including that which describes futile treatment as something that does not benefit the patient as a whole. Furthermore, care may be considered unreasonable if it is excessive and not generally agreed upon. Physician refusal to provide futile or unreasonable care is supported by the ethical principles of nonmaleficence, beneficence, and distributive justice. The last principle is particularly relevant in the current climate of healthcare reform. CONCLUSIONS: Although the issue of physician refusal of requested care has not been resolved by case law or legal statute, it is supported by compelling ethical principles. Physicians are not ethically required to provide futile or unreasonable care, especially to patients who are brain dead, vegetative, critically or terminally ill with little chance of recovery, and unlikely to benefit from cardiopulmonary resuscitation.

Illegitimate tasks are tasks that violate norms for what the employee should do as part of the job, and have been found to harm employees’ well-being. The current research uses a mixed methods design to examine the role of attributions on the two types of illegitimate tasks: unreasonable and unnecessary tasks. A sample of 432 engineers described a specific illegitimate task that was assigned to them, the attributions they made and their response. They also completed a quantitative questionnaire. Results from both the qualitative (event level) and quantitative (person level) portions of our study portray differences in the attributions made to unreasonable and unnecessary tasks, as well as differential negative effects on employees’ emotions. In addition, hostile attribution bias was found to moderate the relationship between illegitimate tasks and negative emotions, particularly for unreasonable tasks. This supports the theoretical basis for illegitimate tasks because unreasonable tasks pose a potentially greater risk to the employee’s self-worth than unnecessary tasks that are more often assigned at random.

In this paper, I attempt to show that mathematical economics is unreasonably ineffective. Unreasonable, because the mathematical assumptions are economically unwarranted; ineffective because the mathematical formalisations imply non-constructive and uncomputable structures. A reasonable and effective mathematisation of economics entails Diophantine formalisms. These come with natural undecidabilities and uncomputabilities. In the face of this, I conjecture that an economics for the future will be freer to explore experimental methodologies underpinned by alternative mathematical structures. The whole discussion is framed within the context of the celebrated Wignerian theme: The Unreasonable Effectiveness of Mathematics in the Natural Sciences.

Generating textual descriptions from visual inputs is a fundamental step towards machine intelligence, as it entails modeling the connections between the visual and textual modalities. For years, image captioning models have relied on pre-trained visual encoders and object detectors, trained on relatively small sets of data. Recently, it has been observed that large-scale multi-modal approaches like CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, provide a strong zero-shot capability on various vision tasks. In this paper, we study the advantage brought by CLIP in image captioning, employing it as a visual encoder. Through extensive experiments, we show how CLIP can significantly outperform widely-used visual encoders and quantify its role under different architectures, variants, and evaluation protocols, ranging from classical captioning performance to zero-shot transfer.

The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10 × or 100 × ? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between ‘enormous data’ and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.

Recent studies have used self-report methods to defend a close associative or causal connection between appraisal and emotion. The present experiments used similar procedures to investigate remembered experiences of reasonable and unreasonable anger and guilt, and of nonemotional other-blame and selfblame. Results suggest that the patterns of appraisal reported for reasonable examples of emotions and for situations where there is a near absence of emotion may be highly similar, but that both may differ significantly from the appraisal profiles reported for unreasonable examples of the same emotions. Further, relevant appraisals were not always identified by participants as the most influential determinants of guilt and anger. These findings demonstrate either that the relationship between certain appraisals and emotions is less consistent than implied in some contemporary versions of appraisal theory, or that there are problems with the validity of existing questionnaire-based measures of the variables in question.

The present study explores the impact of religiosity during the time of the COVID-19 pandemic (March 2020). The focus is on associations between religiosity, coronavirus anxiety, and preventive behavior. Participants were 1,182 U.S. citizens (50% female;20-83 years of age). Highly religious participants scored higher on the somatic component of coronavirus anxiety (emotionality) but lower on the cognitive component (worry). With regard to preventive behavior, highly religious participants reported more unreasonable behavior (e.g., avoiding 5G networks, hoarding toilet paper) than participants with low religiosity;at the bivariate level, there were no differences in reasonable behavior (e.g., physical contact avoidance, frequent handwashing). A comprehensive mediation model showed emotionality-mediated associations between religiosity and unreasonable behavior (positive indirect effect) but also worry-mediated associations between religiosity and reasonable behavior (negative indirect effect). The results remained stable when controlling for relevant sociodemographic variables. The discussion centers on religiosity, information processing, and rationality during a global health crisis situation. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Problems that involve interacting with humans, such as natural language understanding, have not proven to be solvable by concise, neat formulas like F = ma. Instead, the best approach appears to be to embrace the complexity of the domain and address it by harnessing the power of data: if other humans engage in the tasks and generate large amounts of unlabeled, noisy data, new algorithms can be used to build high-quality models from the data.

“and it is possible that there is some secret here which remains to be discovered. ” (C.S. Peirce) There is a story about two friends, who were classmates in high school, talking about their jobs. One of them became a statistician and was working on population trends. He showed a reprint to his former classmate. The reprint started, as usual, with the Gaussian distribution and the statistician explained to his former classmate the meaning of the symbols for the actual population, for the average population, and so on. His classmate was a bit incredulous and was not quite sure whether the statistician was pulling his leg. “How can you know that? ” was his query. “And what is this symbol here? ” “Oh, ” said the statistician, “this is pi. ” “What is that? ” “The ratio of the circumference of the circle to its diameter.” “Well, now you are pushing your joke too far, ” said the classmate, “surely the population has nothing to do with the circumference of the circle.” Naturally, we are inclined to smile about the simplicity of the classmate’s approach. Nevertheless, when I heard this story, I had to admit to an eerie feeling because, surely, the reaction of the classmate betrayed only plain common sense. I was even more confused when, not many days later, someone came to me and expressed his bewilderment 1with the fact that we make a rather narrow selection when choosing

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The effectiveness of reconstructive imaging using the homogeneous transport of intensity equation may be regarded as "unreasonable," because it has been shown to significantly increase signal-to-noise ratio while preserving spatial resolution, compared to equivalent conventional absorption-based imaging techniques at the same photon fluence. We reconcile this surprising behavior by analyzing the propagation of noise in typical in-line holography experiments. This analysis indicates that novel imaging techniques may be designed that produce high signal-to-noise images at low radiation doses without sacrificing spatial resolution.

Peer Reviewed

Mark Wilson; The Unreasonable Uncooperativeness of Mathematics in the Natural Sciences, The Monist, Volume 83, Issue 2, 1 April 2000, Pages 296–314, https://doi

The maximum Nash welfare (MNW) solution --- which selects an allocation that maximizes the product of utilities --- is known to provide outstanding fairness guarantees when allocating divisible goods. And while it seems to lose its luster when applied to indivisible goods, we show that, in fact, the MNW solution is unexpectedly, strikingly fair even in that setting. In particular, we prove that it selects allocations that are envy free up to one good --- a compelling notion that is quite elusive when coupled with economic efficiency. We also establish that the MNW solution provides a good approximation to another popular (yet possibly infeasible) fairness property, the maximin share guarantee, in theory and --- even more so --- in practice. While finding the MNW solution is computationally hard, we develop a nontrivial implementation, and demonstrate that it scales well on real data. These results lead us to believe that MNW is the ultimate solution for allocating indivisible goods, and underlie its deployment on a popular fair division website.

Abstract This chapter focuses on John Rawls's recent approach to liberal political legitimacy. His views on reasonableness and rationality are considered. It is argued that Rawls's legitimation pool for political liberalism is defined precisely in such a way as to exclude those whose prior (illiberal) commitments would lead them to reject political liberalism. The challenge for Rawls is to find good but politically independent reasons for eliminating so-called unreasonable people from the legitimation pool.