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AbstractPlay and games encourage persons to hold the world at a distance, while occasionally challenging its norms through parody, mimicry, and clowning, too. In this article, I offer new ethnography on drinking games among the Nuosu of Southwest China, who distribute penalty shots by injecting them directly into glasses with a syringe. While this revelry may evoke the recent Nuosu history of heroin (ab)use and HIV/AIDS, syringes are used in drinking games to parody the scientific writ large, which encompasses China's modern nation‐building approach, rationality, technological progress, biomedicine, hygiene, urban life, industry, precise standardized measurements, and even the human sciences, including anthropology, ethnology, and ethno‐history. Offering two case studies in which Nuosu ethno‐historians gesture to the scientific and the para‐ethnographic during drinking games, I show that – like ethnography – their play is meant to encourage everyone to stretch the bounds of the occasion by adopting a reflexive stance towards it.

Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic parse trees to identify legally relevant provisions, as well as agents and delegated actions. We undertake two applications. First, we produce a measure of bureaucratic discretion by looking at the level of legislative detail for US states and find that this measure increases after reforms giving agencies more independence. This effect is consistent with an agency cost model, where a more independent bureaucracy requires more specific instructions (less discretion) to avoid bureaucratic drift. Second, we construct measures of delegation to governors in state legislation. Consistent with previous estimates using non-text metrics, we find that executive delegation increases under unified government.

Purpose This paper aims to describe the European Holocaust Research Infrastructure (EHRI) project's ongoing efforts to virtually integrate trans-national archival sources via the reconstruction of collection provenance as it relates to copy collections (material copied from one archive to another) and the co-referencing of subject and authority terms across material held by distinct institutions. Design/methodology/approach This paper is a case study of approximately 6,000 words length. The authors describe the scope of the problem of archival fragmentation from both cultural and technical perspectives, with particular focus on Holocaust-related material, and describe, with graph-based visualisations, two ways in which EHRI seeks to better integrate information about fragmented material. Findings As a case study, the principal contributions of this paper include reports on our experience with extracting provenance-based connections between archival descriptions from encoded finding aids and the challenges of co-referencing access points in the absence of domain-specific controlled vocabularies. Originality/value Record linking in general is an important technique in computational approaches to humanities research and one that has rightly received significant attention from scholars. In the context of historical archives, however, the material itself is in most cases not digitised, meaning that computational attempts at linking must rely on finding aids which constitute much fewer rich data sources. The EHRI project’s work in this area is therefore quite pioneering and has implications for archival integration on a larger scale, where the disruptive potential of Linked Open Data is most obvious. International audience

AbstractBackgroundDuration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge aboutwhenpsychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research. Natural Language Processing (NLP) is a means to addressing this problem by automatically extracting relevant information in a structured form. As a first step, it is important to identify appropriate documents, i.e., those that are likely to include the information of interest. Next, temporal information extraction methods are needed to identify time references for early psychosis symptoms. This NLP challenge requires solving three different tasks: time expression extraction, symptom extraction, and temporal “linking”. In this study, we focus on the first step, using two relevant EHR datasets.ResultsWe applied a rule-based NLP system for time expression extraction that we had previously adapted to a corpus of mental health EHRs from patients with a diagnosis of schizophrenia (first referrals). We extended this work by applying this NLP system to a larger set of documents and patients, to identify additional texts that would be relevant for our long-term goal, and developed a new corpus from a subset of these new texts (early intervention services). Furthermore, we added normalized value annotations (“2011–05”) to the annotated time expressions (“May 2011”) in both corpora. The finalized corpora were used for further NLP development and evaluation, with promising results (normalization accuracy 71–86%). To highlight the specificities of our annotation task, we also applied the final adapted NLP system to a different temporally annotated clinical corpus.ConclusionsDeveloping domain-specific methods is crucial to address complex NLP tasks such as symptom onset extraction and retrospective calculation of duration of a preclinical syndrome. To the best of our knowledge, this is the first clinical text resource annotated for temporal entities in the mental health domain.

Uncertainty was a defining feature of the Brazilian Zika crisis of 2015–2016. The cluster of cases of neonatal microcephaly detected in the country's northeast in the second half of 2015, and the possibility that a new virus transmitted by Aedes mosquitoes was responsible for this new syndrome, created a deep sense of shock and confusion in Brazil and around the world. When in February 2016 the WHO declared a Public Health Emergency of International Concern (PHEIC), it noted that it did so on the basis of what was not known about the virus and its pathogenic potential. To better understand the role that non-knowledge played in the unfolding of the Brazilian Zika crisis we differentiate between three different kinds of uncertainty: global health uncertainty, public health uncertainty, and clinical uncertainty. While these three forms of uncertainty were difficult to disentangle in the early weeks of the crisis, very soon each one began to trace a distinct trajectory. Global health uncertainty centered on the question of the causative link between Zika virus infection and congenital malformations, and was declared resolved by the time the PHEIC was lifted in November 2016. Public health and clinical uncertainty, in contrast, persisted over a longer period of time and did, in some important ways, become entrenched. This taxonomy of uncertainties allows us to explore the systematic nonproduction of knowledge in times of medical emergency, and suggests structural limitations in the framework of “emergency research” that global health institutions have developed to deal with unexpected threats.

AbstractAtrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs.The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing.AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N=10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients.Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts).In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%).Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely-collected EHR data can replicate findings from large-scale curated registries.