Humans and Machines at Work

Monitoring, Surveillance and Automation in Contemporary Capitalism

Phoebe V. Moore, Martin Upchurch, Xanthe Whittaker, Phoebe V. Moore, Martin Upchurch, Xanthe Whittaker

• Publisher: Palgrave Macmillan
• Year: 2017
• Print ISBN:
  9783319582313
• eBook ISBN:
  9783319582320
• Topic: Social Sciences
• Subtopic: Labour Economics
• Index: Social Sciences

This edited collection provides a series of accounts of workers' local experiences that reflect the ubiquity of work's digitalisation. Precarious gig economy workers ride bikes and drive taxis in China and Britain; call centre workers in India experience invasive tracking; warehouse workers discover that hidden data has been used for layoffs; and academic researchers see their labour obscured by a 'data foam' that does not benefit them. These cases are couched in 

historical

accounts of identity and selfhood experiments seen in the Hawthorne experiments and the lineage of automation. This book will appeal to scholars in the Sociology of Work and Digital Labour Studies and anyone interested in learning about monitoring and surveillance, automation, the gig economy and the quantified self in the workplace.

© The Author(s) 2018

Phoebe V. Moore, Martin Upchurch and Xanthe Whittaker (eds.)Humans and Machines at WorkDynamics of Virtual Workhttps://doi.org/10.1007/978-3-319-58232-0_9

Begin Abstract

“Putting It Together, That’s What Counts”: Data Foam, a Snowball and Researcher Evaluation

Penny C. S. Andrews¹

(1)

Sheffield University, Sheffield, UK

Penny C. S. Andrews

Email: [email protected]

End Abstract

Open data, institutional data, personal data and social data can be gathered by a data broker, deemed to be adding value by creating unprecedented combinations. Some or all of the original data may not belong to the broker, but their control of data flows and ability to combine different sources takes the existing data points into something new that can be sold and resold. This new object may be seen as beneficial, where the data donor and/or society receives something in return, or what I call ‘data foam’ , where the new product or service has little or no benefit to anyone other than the seller. This chapter explains the concept of data foam , using the specific example of the increasing use of metrics in researcher evaluation.

The concept of the surveillant assemblage (Haggerty and Ericson 2000) or data double (Poster 1990) is a familiar one. People are aware that their data are profitable to corporations, for marketing and insurance purposes, crime prevention and control and myriad other uses. The privacy intrusion is seen as acceptable, if they receive something in return (Gordon 2014). This is a part of the price we pay for cheaper and more personalised products and services, and has come into its own with the rise of ‘freemium’ apps for mobile devices and the seemingly almost compulsory engagement with platforms from Google and Facebook in order to engage with modern life. Our shadows are always with us.

There are so many data-related metaphors now: data flows , data journeys, data friction, data doubles, data ghosts. Why throw another into the fray? Why ‘data foam’? Well, the various components of the ‘assemblage’ can be agitated again and again to produce a new and shallow layer of dubious value on top of the seemingly reasonable use of data in public statistics or as payment for products and services. This agitated ephemeral layer, this ‘foam’, is another product or service to be sold—not to solve a problem but to create a market or influence decision-making.

Is charging for value-added services really an unproblematic aspect of open data and ‘leaky’ data (social media, institutional/work data, phone data, CCTV , ANPR, data others put out there on our behalf) (Bates 2012)? Who benefits? Whose labour is not compensated or valued? Are platforms really the problem? Is the financialisation of open data, that should be publicly owned and not necessarily exploited for profit (Bates and Goodale 2017), and personal data, that should not be weaponised against a user (Lyon 2003; G.J.D. Smith 2016), the price we have to pay to live and work in the twenty-first century?

Once, my wallet was stolen in a large store in an out-of-town shopping centre. There were no witnesses and, according to the duty manager, the area of the store where I had been standing when it happened was not covered by CCTV—it was an open area with no shelves and therefore no chance of employees or shoplifters damaging or stealing stock or fittings. The security camera in ‘public’ areas does not exist to protect citizens, but to monitor employees and protect property. Workplace monitoring and productivity metrics are again not for the benefit of the surveilled. So combinations of these data sources, frothed up into something new, are used for decision-making that rarely benefits the data creator. It is exacerbating existing problems.

The new objects created from combining sources, this data ‘foam’ on top of the existing flows, can be used for monitoring, marketing, assessment and control. Cambridge Analytica’s algorithms worked with their unique combinations of Facebook data to influence the outcomes of the UK EU referendum vote (‘Brexit’) and the 2016 US Presidential election. Elsevier’s ‘basket of metrics ’ is used for researcher assessment, comparison and employment decisions. The call centre or warehouse performance dashboard and the Bradford Factor for measuring employee absenteeism are so old fashioned now that you can combine video tracking, social media and physical social interactions of employees and persuade them to do corporate wellness wearables such as Corporate Wellness 360 , which offers corporate wellness packages where staff are provided with devices that generate ‘smart data’ and advanced analytics for employers.

Quantification in higher education made its biggest early strides on the student-facing side of the university, providing ‘richer information’ (Williamson 2016) to support ‘learning and teaching’ via learning analytics, recruitment and retention management and course and tutor evaluation (Hall 2016). It crept slowly into the work of the researcher, as the regular research evaluation exercises started to take hold in many countries, with academic tenure, promotion and recruitment committees also enjoying the ‘evidence’ provided by cold hard numbers (Besley and Peters 2009). The quality of research could suddenly be measured by the Impact Factor of the venue in which the outputs were published, the number of times they were cited, a star rating in the research excellence framework (REF) , and the ability of the researcher to bring in grant funding against targets. The backlash against such crude measures (Gruber 2014; Anonymous Academic 2015) has only encouraged the spawning of yet more metrics , bringing in quantification of impact via social media data and other sources (Martín-Martín et al. 2016) and touting the value of Lambert’s (2003) ‘basket of metrics’ for researcher assessment (Clements et al. 2016b).

Metrics are not merely ‘neutral’ statistics as all chapters in the current edited collection maintain. When multiple actors use a measurement, it becomes a visible artefact that can be compared with other artefacts—a material object that did not exist before (Pine and Liboiron 2015; Moore and Robinson 2016). The production and analysis of these artefacts is a profitable service, be it traditional bibliometrics , which serve disciplines such as the humanities very poorly (Thelwall and Delgado 2015; Stelmach and Von Wolff 2011), or the alternative article level metrics commercialised as A...

Table of contents

• Cover
• Frontmatter
• Humans and Machines at Work: Monitoring, Surveillance and Automation in Contemporary Capitalism
• Digitalisation of Work and Resistance
• Deep Automation and the World of Work
• There Is Only One Thing in Life Worse Than Being Watched, and that Is not Being Watched: Digital Data Analytics and the Reorganisation of Newspaper Production
• The Electronic Monitoring of Care Work—The Redefinition of Paid Working Time
• Social Recruiting: Control and Surveillance in a Digitised Job Market
• Close Watch of a Distant Manager: Multi-surveillance by Transnational Clients in Indian Call Centres
• Hawthorne’s Renewal: Quantified Total Self
• “Putting It Together, That’s What Counts”: Data Foam, a Snowball and Researcher Evaluation
• Technologies of Control, Communication, and Calculation: Taxi Drivers’ Labour in the Platform Economy
• Backmatter