scholarly journals Are machines stealing our jobs?

2020 ◽  
Vol 13 (1) ◽  
pp. 153-173 ◽  
Author(s):  
Andrea Gentili ◽  
Fabiano Compagnucci ◽  
Mauro Gallegati ◽  
Enzo Valentini
Keyword(s):  
Cluster Analysis ◽  
International Federation ◽  
Data Driven ◽  
The Political ◽  
Human Labour ◽  
The Future ◽  
Data Driven Approach ◽  
Technological Transition ◽  
Cross Country ◽  
Academic Debate

Abstract This study aims to contribute empirical evidence to the debate about the future of work in an increasingly robotised world. We implement a data-driven approach to study the technological transition in six leading Organisation for Economic Co-operation and Development (OECD) countries. First, we perform a cross-country and cross-sector cluster analysis based on the OECD-STAN database. Second, using the International Federation of Robotics database, we bridge these results with those regarding the sectoral density of robots. We show that the process of robotisation is industry- and country-sensitive. In the future, participants in the political and academic debate may be split into optimists and pessimists regarding the future of human labour; however, the two stances may not be contradictory.

Contour clustering: A field-data-driven approach for documenting and analysing prototypical f0 contours

2021 ◽  
pp. 1-30
Author(s):  
Constantijn Kaland
Keyword(s):  
Cluster Analysis ◽  
User Interfaces ◽  
Data Driven ◽  
Language Documentation ◽  
Cluster Number ◽  
Automatic Data ◽  
Speaker Variability ◽  
The Core ◽  
Data Driven Approach ◽  
Confirmatory Analysis

ABSTRACT This paper reports an automatic data-driven analysis for describing prototypical intonation patterns, particularly suitable for initial stages of prosodic research and language description. The approach has several advantages over traditional ways to investigate intonation, such as the applicability to spontaneous speech, language- and domain-independency, and the potential of revealing meaningful functions of intonation. These features make the approach particularly useful for language documentation, where the description of prosody is often lacking. The core of this approach is a cluster analysis on a time-series of f0 measurements and consists of two scripts (Praat and R, available from https://constantijnkaland.github.io/contourclustering/). Graphical user interfaces can be used to perform the analyses on collected data ranging from spontaneous to highly controlled speech. There is limited need for manual annotation prior to analysis and speaker variability can be accounted for. After cluster analysis, Praat textgrids can be generated with the cluster number annotated for each individual contour. Although further confirmatory analysis is still required, the outcomes provide useful and unbiased directions for any investigation of prototypical f0 contours based on their acoustic form.

La orientación al dato, nuevos desafíos

Tábula ◽  
2021 ◽  
Author(s):  
Miguel Ángel Amutio Gómez
Keyword(s):  
European Union ◽  
Digital Transformation ◽  
Data Driven ◽  
The European Union ◽  
Data Preservation ◽  
The Future ◽  
Data Driven Approach ◽  
New Challenges ◽  
The Impact

La orientación al dato en el contexto de la transformación digital lleva aparejada la aparición de nuevas regulaciones, dinámicas de gobernanza y roles, y servicios, junto con las correspondientes prácticas, instrumentos y estándares. A la vez se suscitan retos en relación con la ciberseguridad y la preservación de los datos. En este artículo se exponen la transformación digital y la orientación al dato, la proyección de lo anterior en la administración digital, el contexto de la Unión Europea, trayectoria y su orientación, aspectos de la interoperabilidad, ciberseguridad y preservación de los datos, cuestiones de gobernanza y roles en la orientación al dato y, finalmente, unas conclusiones. The data-driven approach in the context of digital transformation entails the appearance of new regulations, governance dynamics and roles, and services, together with the corresponding practices, instruments and standards. At the same time new challenges appear in relation to cybersecurity and data preservation. This article presents the digital transformation and data-driven approach, the impact in digital administration, the context of the European Union, trajectory and orientation towards the future, along with aspects of interoperability, cybersecurity and data preservation, as well as issues of governance and roles in data orientation and finally some conclusions.

scholarly journals Teachers and learners in a time of big data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Rachel Buchanan ◽  
Amy McPherson
Keyword(s):  
Digital Data ◽  
Data Driven ◽  
Critical Examination ◽  
The Political ◽  
Educational Systems ◽  
Educational Models ◽  
Technological Transformation ◽  
The Past ◽  
Educational Culture ◽  
The Future

Policy and technological transformation have coalesced to usher in massive changes to educational systems over the past two decades. Teachers’ roles, subjectivities and professional identities have been subject to sweeping changes enabled by sophisticated forms of governance. Simultaneously, students have been recast as ‘learners’; like teachers, learners have become subject to new forms of governance, through technological surveillance and datafication. This paper focuses on the intersection of the metrics driven approach to education and the political as a way to re-think the future of schooling in more explicitly philosophical terms. This exploration starts with a critical examination of constructions of teachers, learners and the digital data-driven educational culture in order to explicate the futures being generated. The trajectory of this future is explored through reference to the techno-educational models currently being developed in Silicon Valley. Drawing on Deleuze’s notion of control societies we contribute to the ongoing philosophical investigation of the datafication of education; a necessary discussion if we are to explore the future implications of schooling in a technologically saturated world. We present consideration of the past, present and future, as three ways of considering alternatives to a datafied education system. Alternative conceptualisations of the future of schooling are possible which offer ways of understanding and politicising what happens when we impose data-driven accountabilities into people’s lives.

Towards a resilient perspective for the future of offshore platforms. Insights from a data driven approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Francesca Loia ◽  
Nunzia Capobianco ◽  
Roberto Vona
Keyword(s):  
Local Governments ◽  
Alternative Energy ◽  
Data Driven ◽  
Environmental Research ◽  
Offshore Platforms ◽  
Territorial Development ◽  
Content Type ◽  
Circular Model ◽  
The Future ◽  
Data Driven Approach

Purpose This study aims to investigate the collective perception regarding the future of offshore platforms and frame the main categories of meanings associated by the community with the investigated phenomenon. Design/methodology/approach A data driven approach has been conducted. The collection of the peoples’ opinions has been realized on two specific social network communities as follows: Twitter and Instagram. The text mining processes carried out a sentiment and a cluster analysis. Findings The sentiment analysis of the most frequent words has been shown. The following four main homogeneous categories of words are emerged in relation to the decommissioning of offshore platforms: technological areas, green governance (GG), circular economy and socio-economic sphere. Research limitations/implications The alternative use of the offshore platforms, including tourism initiatives, aquaculture, alternative energy generation, hydrogen storage and environmental research, could improve the resilience of communities by offering the development of new jobs and the growth of local and innovative green businesses. Practical implications The adoption of a circular model and GG initiatives aims to limit the input of resources and energy, minimize waste and losses, adopt a sustainable approach and realize new social and territorial value. Originality/value The analysis underlines the importance to adopt a systems perspective, which takes into account the social, economic and environmental system as a whole, the different phenomena that occur and the variety of categories of stakeholders, from users to local governments that participate in the territorial development.

Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: An exploratory study of the SPRINT trial

2019 ◽  
Vol 26 (16) ◽  
pp. 1693-1706 ◽  
Author(s):  
Da-ya Yang ◽  
Zhi-qiang Nie ◽  
Li-zhen Liao ◽  
Shao-zhao Zhang ◽  
Hui-min Zhou ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Antihypertensive Treatment ◽  
Cox Regression ◽  
Cardiovascular Outcomes ◽  
Data Driven ◽  
Hypertensive Patients ◽  
Increased Risk ◽  
Data Driven Approach ◽  
Treatment Responses ◽  
Cluster 2

Background Hypertensive patients are highly heterogeneous in cardiovascular prognosis and treatment responses. A better classification system with phenomapping of clinical features would be of greater value to identify patients at higher risk of developing cardiovascular outcomes and direct individual decision-making for antihypertensive treatment. Methods An unsupervised, data-driven cluster analysis was performed for all baseline variables related to cardiovascular outcomes and treatment responses in subjects from the Systolic Blood Pressure Intervention Trial (SPRINT), in order to identify distinct subgroups with maximal within-group similarities and between-group differences. Cox regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for cardiovascular outcomes and compare the effect of intensive antihypertensive treatment in different clusters. Results Four replicable clusters of patients were identified: cluster 1 (index hypertensives); cluster 2 (chronic kidney disease hypertensives); cluster 3 (obese hypertensives) and cluster 4 (extra risky hypertensives). In terms of prognosis, individuals in cluster 4 had the highest risk of developing primary outcomes. In terms of treatment responses, intensive antihypertensive treatment was shown to be beneficial only in cluster 4 (HR 0.73, 95% CI 0.55–0.98) and cluster 1 (HR 0.54, 95% CI 0.37–0.79) and was associated with an increased risk of severe adverse effects in cluster 2 (HR 1.18, 95% CI 1.05–1.32). Conclusion Using a data-driven approach, SPRINT subjects can be stratified into four phenotypically distinct subgroups with different profiles on cardiovascular prognoses and responses to intensive antihypertensive treatment. Of note, these results should be taken as hypothesis generating that warrant further validation in future prospective studies.

Playing by the Rules

Watchdog ◽  
2020 ◽  
pp. 89-104
Author(s):  
Richard Cordray
Keyword(s):  
Financial Crisis ◽  
Mortgage Market ◽  
Data Driven ◽  
The Political ◽  
Financial Protection ◽  
Political Climate ◽  
2008 Financial Crisis ◽  
Data Driven Approach ◽  
The 2008 Financial Crisis ◽  
Entire Economy

In the runup to the 2008 financial crisis, lenders were offering reckless “no doc” mortgages, consumers were making unsustainable choices, and the effects were devastating not only for those lenders and consumers but also for everyone around them. Many unsound practices that lenders engaged in were entirely legal, and Congress directed the Consumer Financial Protection Bureau to write new rules to rein in irresponsible practices to safeguard consumers and the entire economy. Congress gave the bureau only eighteen months to complete major regulations reshaping the mortgage market. This chapter describes how the bureau created these rules, the data-driven approach it used, the role that economists and market analysts played in helping make key choices, and the political climate in which it all occurred.

Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction

2020 ◽  
Vol 10 (16) ◽  
pp. 5696 ◽  
Author(s):  
Samar A. Shilbayeh ◽  
Abdullah Abonamah ◽  
Ahmad A. Masri
Keyword(s):  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Sir Epidemic ◽  
Past Behavior ◽  
The Us ◽  
The Future ◽  
Data Driven Approach ◽  
Expert Input ◽  
Death Cases

Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data.

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