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  • Mark 6:22 pm on September 18, 2019 Permalink | Reply
    Tags: app development, coding, ,   

    Apps and their users: a few initial ontological thoughts 

    It’s important to grasp how much the development cycle for apps and platforms differs from that of software from a previous era. The typical app will go through a far higher number of iterations than a comparable piece of software would have in a pre-smart phone era. There are numerous reasons for this ranging from changes in the operating system are more frequent through to the unpredictability of bugs in an operating environment encompassing numerous elements which are themselves changing. But the most interesting dimension of this continual iteration is the relationship to users it entails. There is a risk of frustrating the users with too many updates, as each one acted upon inevitably consumes time, resources and data, with the latter potentially being in short supply if the user cannot connect to wifi before undertaking the update. However too few updates implies a lack of responsiveness to the emerging community of users, one which might prove fatal if it eventually leads to App Store reviews being swamped with poor reviews and comments about a failure to update the software. Particularly when it comes to apps which involve a sustained relationship, such as cloud based note taking or productivity software, the accusation that a developer is lax in addressing problems or responding to user demands is liable to prove immensely off-putting. Therefore the app development process involves negotiating a relationship with an emerging community of users, iterating the project in real time in relation to a user game which is itself changing. The user group might be growing or shrinking it size. The average level of familiarity with the app might be increasing as users become committed, or it might be decreasing as a small cluster of sustained users are supplemented by an influx of new users whose engagement is at least heretofore shallow. Users might be growing in commitment as the app becomes part of their daily routines, or their decreasing engagement might be the canary in the coal-mine which marks the entry into the death cycle for a once promising service.

    There are many ways in which the user group might be changing at any specific moment in time and this has important implications for the epistemic predicament in which developers find themselves. How do they know what users want? How can they anticipate how users might react? The are two means through which these questions can be answered: the transactional data collected by the app which is available to developers and qualitative engagement with the user group through social media, e-mail or feedback within the app. As we’ve seen, the data collected within the app might appear to be nuanced in terms of the data points which are collected on a specific user but it it also restrictive in that its scope doesn’t extend beyond the app itself.

    However if developers are reliant on social media to manage relationships with their user group then they are inevitably subject to precisely the dynamics which we later analyse as the social media machine. If they use Twitter to engage with a user base then certain voices will be more prominent than others. Accounts with many followers, or a high ratio of followers to followed, will have more weight in the feedback they share than those which are less popular. The ephemeral nature of the platform means that feedback will tend to be offered in particular ways, such as being extremely brief and possibly impatient. A Facebook group has its own unpredictable dynamics, liable to attract only committed users but also to encourage them to express their views in certain ways. It might be that e-mail proves an effective means to interact with users, but there’s a higher threshold for engagement than other platforms. It’s easier and quicker to send a tweet to someone you have not spoken to before than it is an e-mail. It also creates the risk that the developer will receive long, possibly rambling e-mails, consuming more time than might otherwise be needed to discern what the take away message of the interaction should be. It’s not that one platform is intrinsically better than others for these purposes but rather that each involves a mixture of affordances and constraints, inflecting the relationship that its being built through the particular characteristics of the platform being used to build it.

    If we recognise these as two dimensions through which a platform firm builds a relationship with a community of users, it opens up the possible tensions that might exist between them. What if users in a Facebook group are telling you things about the platform which aren’t borne out by the data? What weight should be placed on individual experiences conveyed by e-mail compared to the aggregate trends which can discerned from monitoring user activity.

     

  • Mark 1:52 pm on April 20, 2019 Permalink | Reply
    Tags: coding, , digital capacities, digital competence, , , , huw davies   

    Is digital upskilling the next generation our ‘pipeline to prosperity’? 

    My notes on Davies, H. C., & Eynon, R. (2018). Is digital upskilling the next generation our ‘pipeline to prosperity’?. New Media & Society, 20(11), 3961-3979.

    It’s so rare for a paper to have such a wonderfully informative title. Huw Davies and Rebecca Eynon interrogate this assumption that “teaching young people digital skills and literacies will help advanced market economies compete with their rivals and deliver prosperity” (3961-3962). Computer Science is now part of the Natural Curriculum for all children in England aged 5-14 after a campaign by a range of actors, underscoring the creative dimension of computing alongside its importance to the economy and status as a life skill. Through doing so, “the creative use of technology was assimilated into a ‘set of capacities’ or skills that professionals acquire in order to participate in the labour market” (3962). Digital skills are framed as the “primary antidote to economic decline” and coming disruptive shocks, with the pipeline becoming “the default metaphor in policy discourse to suggest the economy is a machine that feeds on a fixed, constant supply of digitally up-skilled youngsters” (3692). These skills are presented as a way to enhance social mobility, incorporating digital skills into a particularly narrow and nationalistic  understanding of economic need. In the process, caution Davies and Eynon we see a “highly problematic co-option of important intrinsic or civic benefits of digital engagement into economic discourse” (3963).

    Their study was undertaken in two deprived areas of Wales, withe fieldwork in two schools effected by similar inequalities, one in a former mining town and the other in a deprived area of Cardiff. Their questionnaire was administered to one year 9 and one year 10 class each school and one year 12 class in the Cardiff school (the other school had no sixth form). 15% of respondents reported parents who had been to unviersity and around 70% of these parents worked, typically in the manual or service sector. These questionnaires were then supplemented by workshops undertaking as ICT classes in a year 9 and a year 10 group at each school. These activities focused on gaming practice, marketing games and asking students to draw mind maps to represent their digital ecospheres. I thought this was a particularly interesting method and I’ve been thinking recently about how to use creative methods like this to explore people’s platform imaginaries. For the year 9s ICT was compulsory whereas it had been deliberately chosen for the year 10s. The third method was semi structured interviews with 10 students from each year group at each school (n=50) with questions about digital practice, motivations, ambitions and skills. These were used to build a typology of the ways in which young people talk about their technology practice, drawing on the interview and workshop data initially supplemented by additional data from the survey.

    The cyber kid discourse seen as the start “tacitly assumes young people’s motivations and the class of conditions that influence these motivations are (or should be) universal” (3966). it goes hand-in-hand with a tendency to homogenise digital technology seeing it in more or less uniform terms. This is belied by their finding that “Digital technology’s multifunctionality is mobilised by young people who have different personalities and socially shaped motivations, incentives and constraints guiding them” (3966). These are the categories they developed for the taxonomy:

    • Non-conformists: mostly young women, experienced a sense of estrangement from the school’s prevailing culture yet were able to find interlocutors online. The majority were in the 15 years old group and were “using social media as a resource to develop their identity” (3967). Their orientation to digital opportunities was entirely on their own terms, including the possibility of entrepreneurial activity through these means.
    • PC gamers: mostly young men, with a passion for gaming and the technical skills that went with it. This included hands on experience building PCs, with CPUs adequate for the intense graphical demands of modern games. Their interest in PCs came from their experience of the limitations of consoles. Interestingly, many reported that it had initially been a way to spend time with their fathers but as they progressed it became a peer-to-peer activity, suggesting game as male sociality. Furthermore, their fathers were more likely to be technical or professional than other children’s, suggesting a vector of class reproduction. The coding they had been presented to them at school was largely unappealing, yet they engaged in highly technical pursuits ranging from the aforementioned PC building through to YouTube channels, writing games in C++ and some minor consulting.
    • Academic conservatives: mostly female, with a shared commitment to formal education which they saw as more important than digital technology. They framed it as a distraction from these much more important ends. They used social media but were measured and controlled in their digital practice, not having friends who they didn’t also know online. Their aspirations lay in what partisans of the digital future would see it as quintessentially 20th cnetury jobs.
    • Pragmatists: their use of technology was restricted to specific purposes, tending to see it as a means to an end rather than end in itself. For example social media would be used to arrange meet ups or reminisce about those that had taken place in the past. They had often experienced digital exclusion (e.g. “limited money for games their friends played, lack of home access to the Internet, feeling behind in terms of digital skills, or having constrained access to the Internet for safe- guarding purposes” 3972) and their pragmatism could be framed as a response to this.
    • Leisurists: the largest group, tending to see the internet primarily for entertainment and cultural consumption. For them technology is a way of pursuing their interests, often happily leaving them within walled gardens and synchronising devices with parents and family. These activities “tended not to translate into pursuing a passion or developing skills that could be monetised in the digital economy” (3973).

    The digital skills discourse suggests (a) a convergence between the needs of the economy and the needs of young people which can be met through digital skills (b) a denial of alternative motivations for young people that may not feed into this (c) the lack of structural constraints upon where digital skills can take them in the labour market. Their paper is a challenge to “the deterministic discourses that tell young people learning to code would be an act of economic self-interest that will, in turn, defibrillate the economy” (3976). The fact of having coding skills won’t lead to some magical capacity to transcend structural conditions, particularly for young women in a overwhelmingly male dominated industry.

     

     

  • Mark 7:44 pm on March 27, 2019 Permalink | Reply
    Tags: , coding, , design sociology, Phil brooker, ,   

    Programming as Social Science: a case study of @pdbrooker’s surprisingly militant bots 

    My notes on Brooker, P. (2019). My unexpectedly militant bots: A case for Programming-as-Social-Science. The Sociological Review. https://doi.org/10.1177/0038026119840988

    In this thought provoking paper, Phil Brooker takes issue with the scaremongering surroundings bots which positions them as epistemically dangerous due to their quantity and capacity to evade deception. Instead he propose sociologists engage with them as both topic and resource, able to be used critically as a way of intervening in problematic online areas. This entails an upskilling by sociologists, without which any such engagement is likely to be at an abstract distance. He argues that “programming is a vital tool for responding to emerging issues and is potentially transformative across the social sciences in terms of how we understand and intervene in the world” (3).

    Sociology lags behind fields like the digital humanities and software studies for reasons which would be interesting to explore but he offers this intervention as part of a broader project to help close that gap. He argues that we should think of “computer programming as a multipurpose toolkit for understanding and intervening in the (digital) social world in lots of different ways” (7). He begins with an exploration of the social character of design, infamously manifested in bots which reproduce the discriminatory features of the environment they take as stimulus. Drawing on Lupton’a work on design sociology, he draws attention to the affinity of design and sociology, with the former increasingly attending to the social context and the role of users within it. He argues that Di Salvo’s conception of adversarial design has particular relevance, with its focus on the capacity of design to disrupt preconceptions and knock users out of habitual ways of thinking.

    His focus on social media bots responds to “a shift from depicting bots as insidious infections against which platform users must inoculate themselves, to a more nuanced understanding that reflects the different functions bots hold for those that build and use them” (5). The problem of bots is often framed in terms of computational propaganda such that they are seen to be fundamentally deceptive and manipulative, coming from without and requiring decisive intervention” (5), in spite of the useful and fun purposes bots have long served on sites like Reddit and Wikipedia. He argues that using design approaches instead “opens up a space to think about understanding bot–human interaction as playful, creative, positive and/or useful both culturally and sociologically” (6).

    I won’t try to summarise the superb narratives of his two bots and their development. But he positions the first, which randomly generates Facebook updates from a core list of components, in terms of the politics of obfuscation which can be seen in projects like ISP Data Pollution, RuinMyHistory and Noiszy and their significance after the Cambridge Analytica revelations. He explores the latter in terms of the digital picket line, redesigning his Zenbot to proactively inform people it was on strike on the appropriate days, using the relevant hashtag and accompanied by random positive invocations, rather than dispensing zen wisdom on command via twitter. He uses these to signify the potential direction which what he terms *programming as social science* could take for sociology, particularly in terms of its capacity to it help open the notorious black boxes of algorithms and software. On pg 16 he notes other forms PaSS might take:

    engaging with new forms of data, designing new methods to investigate social phenomena, developing interactive/alternative/engaging data visualisations, building applications to nurture innovative forms of interaction with research participants, and so on.

    But a few steps are needed first. We need to acknowledged the lived experience of interacting with bots, as opposed to engaging in abstract generalisations. We need to grapple with the ethical challenges posed by these potential interventions, currently undertaken with a sociological blank canvas. We need to take bot design seriously as a method of generating social knowledge, currently being taken up almost exclusively by non sociologists. I consider myself enthusiastically signed up to Phil’s project here!

     

  • Mark 2:00 pm on July 27, 2016 Permalink | Reply
    Tags: coding, coding skills bubble, , , , ,   

    The limitations on learning to code as a labour market strategy 

    In the last few months I’ve become very interested in the status accorded to coding as a labour market strategy. It’s held up as both individually rational and a viable strategy for governments seeking to grow the human capital of their citizens. However, as Douglas Rushkoff observes in his Throwing Rocks at the Google Bus, we’re likely to see growing numbers of coding jobs outsourced to lower cost countries through computational clearing houses. Furthermore, from Loc 866:

    Besides, learning code is hard, particularly for adults who don’t remember their algebra and haven’t been raised thinking algorithmically. Learning code well enough to be a competent programmer is even harder. Although I certainly believe that any member of our highly digital society should be familiar with how these platforms work, universal code literacy won’t solve our employment crisis any more than the universal ability to read and write would result in a full-employment economy of book publishing.

    Whatever demand for skills currently exists is likely to diminish with time, while available opportunities risk being swamped by aspirants given a wider context of occupational insecurity in which areas seen as focal points for growth are seized upon in an ever quicker fashion.

    There’s an amusing account of 3 scenarios of learning to code here:

    Scenario 1

    Person 1: “I tried to learn to code once. I had a hard time. Life got in the way, and I am no longer trying to learn to code.”

    Marketer: “Coding is easy!”

    Person 1: “What? Oh. Maybe coding is easy after all. Maybe I’m just dumb.”

    Scenario 2

    Person 2: “I want to learn to code, but it sounds hard.”

    Marketer: “Coding is easy!”

    Person 2: “Really?”

    Marketer: “Yes. Buy my course/program/e-book and you’ll be an elite coder in less than a month.”

    Person 2: 

    Shut up and take my money!

    Person 2, one month later: “I thought coding was supposed to be easy. Maybe I’m just dumb.”

    Scenario 3

    Person 3: I have no interest in ever learning to code. I’m a successful manager. If I ever need something coded, I’ll just pay someone to code it for me.

    Marketer: Coding is easy!

    Person 3: Oh, OK. Figures. In that case, I guess I won’t pay those code monkeys very much, or hold their work in very high regard.

    https://medium.freecodecamp.com/one-does-not-simply-learn-to-code-f25bacdc5b62#.cao8vgm6t

     

  • Mark 7:43 pm on April 24, 2016 Permalink
    Tags: , coding, coding economy, ,   

    #YesWeCode Initiative 

    This is really interesting on a number of levels. I don’t want to cast doubt on the value of the project or the motivations underlying it, but I think there are important questions to be asked about the increasing weight placed on the diffusion of coding skills as a way to ameliorate inequality:

    >#YesWeCode (http://www.yeswecode.org) is an initiative that seeks to
    >mobilize tech and social justice leaders to connect 100,000
    >low-opportunity young adults to the skills and experiences they need to
    >access high-paying careers in technology. CNN commentator Van Jones
    >founded the #YesWeCode initiative with the support of Prince, who passed
    >away yesterday.
    >
    >http://www.yeswecode.org/prince
    >
    >Jones elaborated on Prince’s involvement at the 20th Anniversary Essence
    >Festival:
    >
    >”After the Trayvon Martin verdict I was talking to Prince and he said,
    >’You know, every time people see a young black man wearing a hoodie,
    >they think, he’s a thug. But if they see a young white guy wearing a
    >hoodie they think, oh that might be Mark Zuckerberg. That might be a
    >dot-com billionaire.'”
    >
    >”I said, ‘Well, yeah, Prince that’s true but that’s because of racism.’
    >And he said, ‘No, it’s because we have not produced enough black Mark
    >Zuckerbergs. That’s on us. That’s on us. To deal with what we’re not
    >doing to get our young people prepared to be a part of this new
    >information economy.'”
    >
    >Since July 2014, YesWeCode has been focused on three activities:
    >
    >Communicate: In partnership with Oakland, Calfornia-based organizations,
    >#YesWeCode launched an interactive website with a powerful search tool
    >that enables users to find local coding education resources. This tool
    >also helps users find local events and learn more about coding
    >opportunities.
    >
    >Convene: #YesWeCode has convened 100+ coding practitioners and
    >stakeholders in New York, San Francisco, Chicago, and New Orleans.
    >#YesWeCode partnered with Qeyno Labs to host a Start-Up Weekend
    >hackathon in February 2015, focused on uplifting young African-American
    >men and boys.
    >
    >Catalyze: In July, #YesWeCode launched at the 20th Anniversary ESSENCE
    >Festival with a youth-focused hackathon and a headline performance by
    >Prince, before a festival audience of 500,000 people.
    >
    >Just seemed appropriate to post this.

     

  • Mark 6:14 pm on November 12, 2015 Permalink
    Tags: , coding, drug dealer economy, skills bubbles, ,   

    fear of failure and the coding skills bubble 

    I just saw this poster in Euston station. See here for background to this post:

     

     

  • Mark 6:42 am on October 24, 2015 Permalink
    Tags: code academies, coding, , , occupational structure, , teaching coding, tech skills, ,   

    the coding skills bubble 

    As we enter the second machine age, it’s easy to assume that coding skills will be in ever increasing demand. But this TechCrunch feature suggests both that the skills shortage will likely prove fleeting, due to the impending automation of much coding, as well as that bullshit abounds in schemes which aim to address the contemporary shortfall:

    It’s an ingenious business model. There’s a dearth of skilled coders in the marketplace to fill the five million computing jobs available in this country. For somewhere between free and $36,000, you learn to program computers in less than a year. If you’re one of the lucky few, you will hit your aha moment with programming and develop a personal passion for it, as well land a real job.

    In 15 years, those hard-won skills will be obsolete — if they ever stuck in the first place. Despite their promises, coding academies don’t manufacture coders. They cast wide nets to discover new talent that has not yet been exposed to code. Most people don’t find coding enthralling or interesting enough to continue to pursue it as a career. Given the changing nature of software, they probably shouldn’t.

    …

    I see coding shrinking as a widespread profession. Not because software is going away, but because the way we build software will fundamentally change. Technology for software creation without code is already edging toward mainstream use. Visual content creation tools such as Scratch, DWNLD and Telerik will continue to improve until all functionality required to build apps is available to consumers — without having to write a line of code.

    Who needs to code when you can use visual building blocks or even plain English to describe intent? Advances in natural-language processing and conceptual modeling will remove the need for traditional coding from app development. Software development tools will soon understand what you mean versus what you say. Even small advances in disambiguating intent will pay huge dividends. The seeds are already planted, from the OpenCog project to NLTK natural-language processing to MIT’s proof that you can order around a computer in your human language instead of code.

    http://techcrunch.com/2015/10/23/coding-academies-are-nonsense/

    Granted, the author has a vested interest as the developer of one of these automation tools. But he makes an interesting point about the growth of what might be conceived as a coding skills bubble, with 175 percent growth in US academies between 2013-14 and an estimated $2.2 million revenue per school. There’s a lot of money to be made here, with a growing stream of people gravitating towards one of the few occupations popularly understood to be entering a boom time. But those willing and able to make money from providing such an education are disproportionally able to shape public perceptions of coding as an occupation, as well as having an obvious financial incentive to do so. Why entertain challenging questions about the future of coding as an occupation when there’s so much money to be made now?

    The way myths about coding spread seems strikingly similar to other spheres of the digital economy. The TechCrunch author has a wonderfully succinct phrase: publicize successful outliers to propagate the illusion. The visibility of those who have ‘made it’, who are living the dream and doing what they love, licenses commitment and investment by a much greater cohort who have no prospect of doing so. Sound familiar? I’d love to explore this more rigorously at a later date, because the prospect of a homologous opportunity structure between coding and much of the creative industries is a fascinating hypothesis. Now what about data science? Is there an equivalent data science bubble developing?

     

  • Mark 12:26 pm on February 6, 2014 Permalink | Reply
    Tags: coding, , year of code   

    Government coding tsar can’t code, explain what coding is or convey why it’s important 

    <bangs head repeatedly on desk>

    Starts around 6 minutes. I thought the R4 interview on Today was actually worse than this.

     

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