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This article examines the institutional pathology of corruption within the Nigerian Navy, and its broader manifestation and implications within Nigeria’s maritime industry. Beginning with an examination of the disappearance of the vessel, MT African Pride, in October 2003, the article reflects on the African Pride case to show that far from being a one-off incident, it was indicative of the opportunism and criminality that naval operations within the oil-rich Niger Delta have generated. Other primary case studies relevant to corruption within Nigeria’s maritime industry, and interrogated by the article, include the Nigerian Ports Authority (NPA) and the Nigerian Maritime Administration and Safety Agency (NIMASA). The article shall also examine a range of secondary case studies. These are relatively brief meditations on incidents, individuals and maritime industry private contractors and government parastatals, which are relevant to the debate to naval and maritime corruption in Nigeria.

Some harm reduction strategies encourage individuals to switch from a harmful addictive good to a less harmful addictive good. This approach is controversial, with advocates claiming it helps switching to a less harmful substance, and opponents claiming it may lead to new substance abuse. This paper builds on theories of addiction to model the introduction of a harm reduction method, and it demonstrates when each side is correct, depending on the enjoyableness of the harm reduction method, the addictiveness of the harm reduction method, and the substitutability with the original addictive good.

This article brings to bear findings from the debate on the boundary problem in democratic theory on discussions of workplace democracy to argue that workplace democrats’ focus on workers is unjustified and that more constituencies will have to be included in any prospective scheme of workplace democracy. It thereby provides a valuable and underdiscussed perspective on workplace democracy that goes beyond the debate’s usual focus on the clarification and justification of workplace democrats’ core claim. It also goes beyond approaches like stakeholder theory in law and economics that determine decision-making rights without taking into account genuinely democratic considerations. My discussion proceeds by considering three principles for inclusion from democratic theory for the specific case of the corporation. I submit that two of them, the all-coerced and the all-subjected principle, are not appropriate for this specific case, because they cannot capture the distinguishing features of the corporation. The all-affected principle however is appropriate but has a very wide range. I further argue that this is not as big of a problem as it first might seem and that this principle is still the most appropriate for defining the demos of the democratic corporation. The article closes by pointing out the consequences of this result for the workplace democracy debate and for the legitimacy of the market as a coordination mechanism.

Commentators claim that drug repositioning (ie developing new uses for authorised drugs) significantly slows when generics are authorised and, therefore, law reform is necessary to encourage more R&D. This study empirically examines this claim by analysing records of clinical trials. It finds that once generics are authorised: i) commercial trials continue at ‘active’ rates for approximately half of the drugs studied, and ii) the number of hospital and university trials actually increases. These findings cast doubt on whether additional incentives are needed. They also indicate that a more effective way to reposition drugs is for recently established government programmes to embrace IP strategies and leverage the hospital and university trials as an R&D pipeline.Keywords: drug repositioning, patent law, regulatory protection, clinical trials, empirical study, generic drugs.

Abstract In current research, there is a widespread idea that the history of the universal basic income proposal begins with Thomas Paine and his famous pamphlet Agrarian Justice, published in Paris in 1797 in the context of the French Revolution and under its intellectual influence. In this article, the author rescues a largely unknown text by Paine published without a title under the pen name “Amicus” in the Pennsylvania Magazine in 1775. The transcript demonstrates that he was already reflecting on the proposal at least twenty years earlier, shortly after he arrived in the American colonies. The debates and discussions in which Paine wants to intervene cannot be the same in Wilkes's England as in revolutionary France. Locating the proposal of a universal basic income in this context has important implications for the history of its conception. It compels us to highlight the importance of Paine's experience as an exciseman in England, as well as the context of popular radicalism and religious dissent that prevailed in the country at the start of the Industrial Revolution.

Figurative language is part of our everyday life where meaning is communicated indirectly. Metaphor is among the major figurative devices that are commonly encountered in interactions. The aim of this study is to investigate the use of metaphors to conceptualize abstract concepts namely that of anger in both English and Arabic. To this extent, a corpus consisting of metaphorical expressions denoting anger in both languages was utilized. These expressions were classified based on their metaphorical mappings and later on analysed using Kövecses’ (2002) framework. Results indicated that even though the two languages share a number of anger conceptualizations, they had certain differences. These differences were attributed to difference in language, in the sense that language genius and vocabulary repertoire often influence the conceptualization process. Culture, too, proved to be another source of as elements like climate and life style manipulate the conceptualization process. Note that the study of metaphorical conceptualization of emotions in general and anger specifically is underestimated in Arabic and further research is required

Severe maternal morbidity and mortality remain public health priorities in the United States, given their high rates relative to other high-income countries and the notable racial and ethnic disparities that exist. In general, accurate risk stratification methods are needed to help patients, providers, hospitals, and health systems plan for and potentially avert adverse outcomes.Our objective was to understand if machine learning methods with natural language processing of history and physical notes could identify a group of patients at high risk of maternal morbidity on admission for delivery without relying on any additional patient information (eg, demographics and diagnosis codes).This was a retrospective study of people admitted for delivery at 2 hospitals (hospitals A and B) in a single healthcare system between July 1, 2016, and June 30, 2020. The primary outcome was severe maternal morbidity, as defined by the Centers for Disease Control and Prevention; furthermore, we examined nontransfusion severe maternal morbidity. Clinician documents designated as history and physical notes were extracted from the electronic health record for processing and analysis. A bag-of-words approach was used for this natural language processing analysis (ie, each history or physical note was converted into a matrix of counts of individual words (or phrases) that occurred within the document). The least absolute shrinkage and selection operator models were used to generate prediction probabilities for severe maternal morbidity and nontransfusion severe maternal morbidity for each note. Model discrimination was assessed via the area under the receiver operating curve. Discrimination was compared between models using the DeLong test. Calibration plots were generated to assess model calibration. Moreover, the natural language processing models with history and physical note texts were compared with validated obstetrical comorbidity risk scores based on diagnosis codes.There were 13,572 delivery encounters with history and physical notes from hospital A, split between training (AIn this practical application of machine learning, we demonstrated the capabilities of natural language processing for the prediction of severe maternal morbidity based on provider documentation inherently generated at the time of admission. This work should serve as a catalyst for providers, hospitals, and electronic health record systems to explore ways that artificial intelligence can be incorporated into clinical practice and evaluated rigorously for their ability to improve health.