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How to speak tech : the non-techie's guide to key technology concepts
\"Things you've done online: ordered a pizza, checked the weather, booked a hotel, and reconnected with long-lost friends. Now it's time to find out how these things work. Vinay Trivedi peels back the mystery of the Internet, explains it all in the simplest terms, and gives you the knowledge you need to speak confidently when the subject turns to technology. This revised second edition of How to Speak Tech employs the strategy of the popular first edition: through the narrative of setting up a fictitious startup, it introduces you to essential tech concepts. New tech topics that were added in this edition include the blockchain, augmented and virtual reality, Internet of Things, and artificial intelligence. The author's key message is: technology isn't beyond the understanding of anyone. By breaking down major tech concepts involved with a modern startup into bite-sized chapters, the author's approach helps you understand topics that aren't always explained clearly and shows you that they aren't rocket science\"-- Provided by publisher.
Book
A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research
This paper discusses the value of context in theory development in information systems (IS) research. We examine how prior research has incorporated context in theorizing and develop a framework to classify existing approaches to contextualization. In addition, we expound on a decomposition approach to contextualization and put forth a set of guidelines for developing context-specific models. We illustrate the application of the guidelines by constructing and comparing various context-specific variations of the technology acceptance model (TAM)-i.e., the decomposed TAM that incorporates interaction effects between context-specific factors, the extended TAM with context-specific antecedents, and the integrated TAM that incorporates mediated moderation and moderated mediation effects of context-specific factors. We tested the models on 972 individuals in two technology usage contexts: a digital library and an agile Web portal. The results show that the decomposed TAM provides a better understanding of the contexts by revealing the direct and interaction effects of context-specific factors on behavioral intention that are not mediated by the TAM constructs of perceived usefulness and perceived ease of use. This work contributes to the ongoing discussion about the importance of context in theory development and provides guidance for context-specific theorizing in IS research.
Journal Article
Guidance on terminology, application, and reporting of citation searching: the TARCiS statement
Evidence syntheses adhering to systematic literature searching techniques are a cornerstone of evidence based healthcare. Beyond term based searching in electronic databases, citation searching is a prevalent search technique to identify relevant sources of evidence. However, for decades, citation searching methodology and terminology has not been standardised. An evidence guided, four round Delphi consensus study was conducted with 27 international methodological experts in order to develop the Terminology, Application, and Reporting of Citation Searching (TARCiS) statement. TARCiS comprises 10 specific recommendations, each with a rationale and explanation on when and how to conduct and report citation searching in the context of systematic literature searches. The statement also presents four research priorities, and it is hoped that systematic review teams are encouraged to incorporate TARCiS into standardised workflows.
Journal Article
SNOMED CT standard ontology based on the ontology for general medical science
Background
Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT.
Method
Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS).
Results
The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at
http://bioportal.bioontology.org/ontologies/SCTO/
.
Conclusion
The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.
Journal Article
Generative Innovation: A Comparison of Lightweight and Heavyweight IT
This paper proposes a simple terminology for understanding and dealing with two current phenomena; we suggest calling them heavyweight and lightweight IT. Heavy-weight IT denotes the well-established knowledge regime of large systems, developing ever more sophisticated solutions through advanced integration. Lightweight IT is suggested as a term for the new knowledge regime of mobile apps, sensors and bring-your-own-device, also called consumerisation and Internet-of-Things. The key aspect of lightweight IT is not only the cheaper and more available technology compared with heavyweight IT, but the fact that its deployment is frequently done by users or vendors, bypassing the IT departments. Our theoretical lens is generativity, the idea that complex phenomena arise from interactions among basic elements. In the context of IT, generativity helps to explain the creative potential of flexible digital technology for knowledgeable professionals and users. The research questions are: how is generativity different in heavyweight and lightweight IT, and what is the generative relationship between heavyweight and lightweight IT? These questions were investigated through a study of four cases in the health sector. Our findings show that (i) generativity enfolds differently in heavyweight and lightweight IT and (ii) generativity in digital infrastructures is supported by the interaction of loosely coupled heavyweight and lightweight IT. The practical design implication is that heavyweight and lightweight IT should be only loosely integrated, both in terms of technology, standardisation and organisation.
Journal Article
Nociplastic pain: towards an understanding of prevalent pain conditions
Nociplastic pain is the semantic term suggested by the international community of pain researchers to describe a third category of pain that is mechanistically distinct from nociceptive pain, which is caused by ongoing inflammation and damage of tissues, and neuropathic pain, which is caused by nerve damage. The mechanisms that underlie this type of pain are not entirely understood, but it is thought that augmented CNS pain and sensory processing and altered pain modulation play prominent roles. The symptoms observed in nociplastic pain include multifocal pain that is more widespread or intense, or both, than would be expected given the amount of identifiable tissue or nerve damage, as well as other CNS-derived symptoms, such as fatigue, sleep, memory, and mood problems. This type of pain can occur in isolation, as often occurs in conditions such as fibromyalgia or tension-type headache, or as part of a mixed-pain state in combination with ongoing nociceptive or neuropathic pain, as might occur in chronic low back pain. It is important to recognise this type of pain, since it will respond to different therapies than nociceptive pain, with a decreased responsiveness to peripherally directed therapies such as anti-inflammatory drugs and opioids, surgery, or injections.
Journal Article
Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology
Background
In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize terminology through the Adverse Event Terminology Collection of the Japan Federation of Medical Device Associations (JFMDA). Internationally, the Adverse Event Terminology of the International Medical Device Regulators Forum (IMDRF-AET) provides a standardized terminology collection in English. Mapping between the JFMDA terminology collection and the IMDRF-AET is critical to international harmonization. However, the process of translating the terminology collections from English to Japanese and reconciling them is done manually, resulting in high human workloads and potential inaccuracies.
Objective
The purpose of this study is to investigate the optimal machine translation model for the IMDRF-AET into Japanese for the part of a function for the automatic terminology mapping system.
Methods
English-Japanese parallel data for IMDRF-AET published by the Ministry of Health, Labor and Welfare in Japan was obtained from 50 sentences randomly extracted from the terms and their definitions. These English sentences were fed into the following machine translation models to produce Japanese translations: mBART50, m2m-100, Google Translation, Multilingual T5, GPT-3, ChatGPT, and GPT-4. The evaluations included the quantitative metrics of BiLingual Evaluation Understudy (BLEU), Character Error Rate (CER), Word Error Rate (WER), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Bidirectional Encoder Representations from Transformers (BERT) score, as well as qualitative evaluations by four experts.
Results
GPT-4 outperformed other models in both the quantitative and qualitative evaluations, with ChatGPT showing the same capability, but with lower quantitative scores, in the qualitative evaluation. Scores of other models, including mBART50 and m2m-100, lagged behind, particularly in the CER and BERT scores.
Conclusion
GPT-4’s superior performance in translating medical terminology, indicates its potential utility in improving the efficiency of the terminology mapping system.
Journal Article
Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter
Doctors and nurses in these weeks and months are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. The discourse around the current epidemic makes use of war-related metaphors too, not only in public discourse and in the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a large corpus tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY frame covers a wider portion of the corpus, among the figurative frames WAR, a highly conventional one, is the frame used most frequently. Yet, this frame does not seem to be apt to elaborate the discourse around some aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options-or a metaphor menu-may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and beliefs during the current pandemic.
Journal Article
Automatic jargon identifier for scientists engaging with the public and science communication educators
Scientists are required to communicate science and research not only to other experts in the field, but also to scientists and experts from other fields, as well as to the public and policymakers. One fundamental suggestion when communicating with non-experts is to avoid professional jargon. However, because they are trained to speak with highly specialized language, avoiding jargon is difficult for scientists, and there is no standard to guide scientists in adjusting their messages. In this research project, we present the development and validation of the data produced by an up-to-date, scientist-friendly program for identifying jargon in popular written texts, based on a corpus of over 90 million words published in the BBC site during the years 2012-2015. The validation of results by the jargon identifier, the De-jargonizer, involved three mini studies: (1) comparison and correlation with existing frequency word lists in the literature; (2) a comparison with previous research on spoken language jargon use in TED transcripts of non-science lectures, TED transcripts of science lectures and transcripts of academic science lectures; and (3) a test of 5,000 pairs of published research abstracts and lay reader summaries describing the same article from the journals PLOS Computational Biology and PLOS Genetics. Validation procedures showed that the data classification of the De-jargonizer significantly correlates with existing frequency word lists, replicates similar jargon differences in previous studies on scientific versus general lectures, and identifies significant differences in jargon use between abstracts and lay summaries. As expected, more jargon was found in the academic abstracts than lay summaries; however, the percentage of jargon in the lay summaries exceeded the amount recommended for the public to understand the text. Thus, the De-jargonizer can help scientists identify problematic jargon when communicating science to non-experts, and be implemented by science communication instructors when evaluating the effectiveness and jargon use of participants in science communication workshops and programs.
Journal Article
Managing linguistic obstacles in multidisciplinary, multinational, and multilingual research projects
Environmental challenges are rarely confined to national, disciplinary, or linguistic domains. Convergent solutions require international collaboration and equitable access to new technologies and practices. The ability of international, multidisciplinary and multilingual research teams to work effectively can be challenging. A major impediment to innovation in diverse teams often stems from different understandings of the terminology used. These can vary greatly according to the cultural and disciplinary backgrounds of the team members. In this paper we take an empirical approach to examine sources of terminological confusion and their effect in a technically innovative, multidisciplinary, multinational, and multilingual research project, adhering to Open Science principles. We use guided reflection of participant experience in two contrasting teams—one applying Deep Learning (Artificial Intelligence) techniques, the other developing guidance for Open Science practices—to identify and classify the terminological obstacles encountered and reflect on their impact. Several types of terminological incongruities were identified, including fuzziness in language, disciplinary differences and multiple terms for a single meaning. A novel or technical term did not always exist in all domains, or if known, was not fully understood or adopted. Practical matters of international data collection and comparison included an unanticipated need to incorporate different types of data labels from country to country, authority to authority. Sometimes these incongruities could be solved quickly, sometimes they stopped the workflow. Active collaboration and mutual trust across the team enhanced workflows, as incompatibilities were resolved more speedily than otherwise. Based on the research experience described in this paper, we make six recommendations accompanied by suggestions for their implementation to improve the success of similar multinational, multilingual and multidisciplinary projects. These recommendations are conceptual drawing on a singular experience and remain to be sources for discussion and testing by others embarking on their research journey.
Journal Article