Fulcra

→ A senior official said that there was a slight distinction between research — which is crucial in the NIRF framework — and innovation: research produces new knowledge while innovation puts that knowledge to use Sep 4, 2018 highlights & Innovation A senior official said that there was a slight distinction between research – which is crucial in the NIRF framework – and innovation: research produces new knowledge while innovation puts that knowledge to use.ARIIA – named after Atal Bihari Vajpayee – will focus on: budget expenses and revenues generated; facilitating access to advance centres; ideas of entrepreneurship; innovation ecosystems supported through teaching and learning; and innovative solutions to improve governance of the institution. — https://www.thehindu.com/news/national/new-ranking-based-on-innovation/article24823285.ece

→ Litzsinger agrees: “To me, cyberpunk does feel inherently political in that its protagonists almost always operate on the fringes of the law, whether because of criminal activity or the inability for the law to keep up with technology Sep 3, 2018 highlights Litzsinger agrees: “To me, cyberpunk does feel inherently political in that its protagonists almost always operate on the fringes of the law, whether because of criminal activity or the inability for the law to keep up with technology. It can challenge us to think about the difference between something that is legal and something that is moral, and you will find a common thread of rebellion against ‘the system’ in a lot of cyberpunk narratives.” — https://www.polygon.com/features/2018/8/30/17796680/cyberpunk-2077-history-blade-runner-neuromancer

→ I personally think that any cyberpunk work worthy of the name needs to show that dehumanizing, unequal relationship of power and politics as part of its makeup,” says Pondsmith Sep 3, 2018 highlights I personally think that any cyberpunk work worthy of the name needs to show that dehumanizing, unequal relationship of power and politics as part of its makeup,” says Pondsmith. “You don’t raise hell in a future where things are a Star Trekkian Utopia — you raise hell when all the forces in power are arrayed against you personally, and you have to fight back. — https://www.polygon.com/features/2018/8/30/17796680/cyberpunk-2077-history-blade-runner-neuromancer

→ “Body modification is a great avenue for empowering stories for groups routinely denied bodily autonomy: disabled people, trans people, women as a whole, etc Sep 3, 2018 highlights & People “Body modification is a great avenue for empowering stories for groups routinely denied bodily autonomy: disabled people, trans people, women as a whole, etc.,” says Yawns. “The problem is that utopianism clashes with the impoverished lives cyberpunk depicts, immediately raising the question of who can afford these freedoms.
“Enabling bodily autonomy, alteration and restored function is a great thing but as things stand, access for the majority means debt or servitude to malicious corporate monopolies,” says Yawns. “Anyone who’s experienced tech industry practices of planned obsolescence and covert data collection on their phone can imagine what these companies might do given access your cybernetic limbs, let alone your whole nervous system.
“Liberating tech is often made into a yoke by its social context.”
That last part is the biomechanically-enhanced heart of cyberpunk. William Gibson has often summed it up in interviews: “the future is already here — it’s just not very evenly distributed.” Cyberpunk worlds are about the gap between those who have access to their futuristic technologies and those who don’t — a gap that’s often expressed literally, in the verticality of its mega-cities. — https://www.polygon.com/features/2018/8/30/17796680/cyberpunk-2077-history-blade-runner-neuromancer

→ “It is a setting that is focused on the human experience, and how far we can push the limits of both technology and ourselves,” says Litzsinger Sep 2, 2018 highlights “It is a setting that is focused on the human experience, and how far we can push the limits of both technology and ourselves,” says Litzsinger.
The writers who laid the foundation of cyberpunk looked at the accelerating pace of change in the late 20th century, and understood that technology would forever be an inseparable part of the human experience. This is still what makes the genre stand apart from other branches of sci-fi: the way it considers the social impact of technology on everyday life. — https://www.polygon.com/features/2018/8/30/17796680/cyberpunk-2077-history-blade-runner-neuromancer

→ Certain central themes spring up repeatedly in cyberpunk Sep 2, 2018 highlights

”Certain central themes spring up repeatedly in cyberpunk. The theme of body invasion: prosthetic limbs, implanted circuitry, cosmetic surgery, genetic alteration. The even more powerful theme of mind invasion: brain-computer interfaces, artificial intelligence, neurochemistry â€“ techniques radically redefining the nature of humanity, the nature of the self.

Cyberpunk meshes these advanced technologies with more down-to-earth concerns like drugs, dive bars and desperation that turn people to crime. The ruling powers of cyberpunk worlds are almost always immense corporations who control access to technology. The protagonists tend to be outsiders â€” criminals and noir-style antiheroes â€” who exist on the margins of society. Thereâ€™s an oft-quoted maxim by Sterling that sums it up nicely: ‘Lowlife and high-tech.’”

https://www.polygon.com/features/2018/8/30/17796680/cyberpunk-2077-history-blade-runner-neuromancer

→ design can be directly weaponised by the design team itself Sep 1, 2018 highlights & Design design can be directly weaponised by the design team itself. — https://flowingdata.com/2018/08/30/weaponised-design/

→ there’s a lot of potential in collaborating to illuminate the systems that create data Sep 1, 2018 highlights & Science & Systems there’s a lot of potential in collaborating to illuminate the systems that create data. Part of that potential, I think, will be realized by leveraging the different epistemological assumptions behind our respective approaches. For example, there is unquestionable value in using statistical models as a lens to interpret and forecast sociocultural trends—both business value and value to growing knowledge more generally. But that value is entirely dependent on the quality of the alignment between the statistical model and the sociocultural system(s) it is built for. When there are misalignments and blind spots, the door is opened to validity issues and negative social consequences, such as those coming to light in the debates about fairness in machine learning. There are real disconnects between how data-intensive systems currently work, and what benefits societies. — https://www.epicpeople.org/data-science-and-ethnography/

→ TYE: We touched on data provenance earlier, but I want to come back to it from the perspective of quantitative data Aug 31, 2018 highlights & Science & Systems TYE: We touched on data provenance earlier, but I want to come back to it from the perspective of quantitative data. In particular, I think it is critical to keep in mind that the systems that generate quantitative data are necessarily embedded in socio-technical systems. The technological elements of those systems (electronic sensors, software-based telemetry, etc.) are designed, manufactured, and maintained by sociocultural factors. So, a data scientist who is diligently trying to understand where their data comes from in order to interpret it, will sooner or later need to understand sociocultural phenomena that produced data, even if that understanding is more meta-data than data. It would make sense to co-develop rubrics for assessing the quality of data generated by socio-technical systems. Shining a bright light on the deepest lineage of data that impacts business or design decisions is important for everyone involved. Such assessments could lead to more cautious ways of using data, or be used in efforts to improve the explainability of technical systems. — https://www.epicpeople.org/data-science-and-ethnography/

→ DAWN: I’m always curious about how data scientists measure the consistency or sensitivity of results from datasets Aug 31, 2018 highlights & Science DAWN: I’m always curious about how data scientists measure the consistency or sensitivity of results from datasets. You have a notion of confidence intervals that communicates in a shorthand way “this is the size of grain of salt you have to take.” Ethnography doesn’t look at the world probabilistically, so we can never say, “9 of 10 times this will be the case.” But there are patterns, and those patterns can be relied upon for some purposes but not others. Even though we have messy complicated debates about how culture “scales” (which isn’t the same thing as reliability of results, but it’s related), we still don’t have clear ways to communicate to clients “this is the size of the grain of salt you need to take.” — https://www.epicpeople.org/data-science-and-ethnography/

→ https://www. Aug 31, 2018 highlights & Science But a key difference is that ethnographic work critically assesses the role of the researchers as an explicit, expected part of the research process. If data science projects were truly determined by the data alone (sensor data, click data and so forth), then repeated analyses should yield identical results. They don't. More light has been shed on this recently and is captured by concepts like "p-hacking". Minimally, it's clear that data science processes could benefit from more documentation and critical reflection on the effect of the data scientist themselves. The ethnographer's ability to identify and explicate researcher biases and social pressures could be helpful. — https://www.epicpeople.org/data-science-and-ethnography/

→ TYE: One thing I’ve observed about ethnography is that ethnographers often collect metadata simultaneously to collecting data—e Aug 31, 2018 highlights & Science TYE: One thing I’ve observed about ethnography is that ethnographers often collect metadata simultaneously to collecting data—e.g., taking notes on why they might have made certain observations instead of others, how the observations align or conflict with their expectations, etc. Provenance is built-in. The equivalent metadata about provenance might be recorded post hoc for the data scientist, or she might have to create it by talking to the stakeholders who did the collection.
DAWN: We don’t make hard distinctions between metadata and data because you don’t know which is which until you do the analysis, but the provenance is definitely still there. — https://www.epicpeople.org/data-science-and-ethnography/

→ the research process is somewhat similar, from what I have experienced. Aug 30, 2018 highlights & Science

the research process is somewhat similar, from what I have experienced. The three main steps in the data science process are:

data sourcing—more than mere access, it’s also about understanding lineage and assessing quality and coverage;
data transformation—from filtering and simple arithmetic transformations to complex abductions like predictions and unsupervised clustering; and
results delivery—both socially and programmatically (i.e., as lines of code).

— https://www.epicpeople.org/data-science-and-ethnography/

→ While both areas have a core set of expectations, they both have to extend beyond their core in order to deal with data about social life—data which has very real social consequences Aug 30, 2018 highlights & Science & Social Dawn: … While both areas have a core set of expectations, they both have to extend beyond their core in order to deal with data about social lifeâ€”data which has very real social consequences.

TYE: This is all the more true in industry contexts, where we often have to make social decisions, or design decisions, regardless of expertise.

DAWN: One difference is that in many data science scenarios, the available data has already been collected, whereas most ethnographic projects include field research time to gather new data.

TYE: Although this tendency doesnâ€™t hold true all the time, it is a common expectation, and that expectation results in a divergent initial perspective on projects: data scientists often think about working within the available datasets while ethnographers tend to begin their projects by thinking expansively about what dataset could be created, or should be created given the state of the art of the relevant discipline (anthropology, sociology and so forth). This difference in perspectives leads to different attribution models for the results. Data scientists will often describe their results as derived from the data (even if the derivation is complex and practically impossible to trace). Data scientists will readily recognize that they made decisions throughout the project that impacted the results, but will often characterize these decisions as being determined by the data (or by common and proven analyses of the data). You have a totally different way of dealing with that.

DAWN: Yes, for sure. Itâ€™s all coming from â€œthe dataâ€ but ethnographers themselves are a part of the data. A crucial part. If you were an active part of its creationâ€”if you were there, having conversations with people, looking them in the eye as they try to make sense of your presenceâ€”you just canâ€™t see it any other way. Itâ€™s unavoidable. Youâ€™re also aware of all of the other contingent factors involved in the data you collected in that moment. So we have to be explicitly reflective and critical of how our social position influenced the results.

https://www.epicpeople.org/data-science-and-ethnography/

→ Data science, across its variety of forms, is rooted in statistical calculations—involving both the technical knowledge and skill to assess the validity and applicability of these calculations, and the knowledge and skill to implement software or programming functions that execute the calculations Aug 29, 2018 highlights & Science Data science, across its variety of forms, is rooted in statistical calculations—involving both the technical knowledge and skill to assess the validity and applicability of these calculations, and the knowledge and skill to implement software or programming functions that execute the calculations. Underpinning the application of statistical calculations are assumptions about systemic structures and their dynamics—e.g., whether or not entities or events operate independently from one another, whether the variability of measurements, relative to an assumed or imputed trend or structure, is “noise” adhering to a separate set of rules (or not), and so on. Historically, these skill sets and conceptions of reality have been most heavily utilized in scientific inquiry, in finance and insurance, and business operations research (e.g., supply chain management and resource allocation). More recently, data science has expanded into a much larger set of domains: marketing, medicine, entertainment, education, law, etc. This expansion has shifted a large portion of data scientists toward data about people—some of that data is directly generated, like emails and web searches, some of it is sensed, like location or physical activity. — https://www.epicpeople.org/data-science-and-ethnography/

→ Ethnography is now used across anthropology, sociology, marketing, strategy, design, and other fields, but regardless of where it’s used, the core is about understanding people’s beliefs and behaviors and how these change over time Aug 29, 2018 highlights & People & Science & Systems Ethnography is now used across anthropology, sociology, marketing, strategy, design, and other fields, but regardless of where it’s used, the core is about understanding people’s beliefs and behaviors and how these change over time. Ethnography is a research skill that makes it possible to see what the world looks like from inside a particular context. If “man [sic] is an animal suspended in webs of significance he himself has spun" (Geertz), this skill involves systematically tracing out the logic of those webs, and examining how those webs structure what people do and think. Depending on the domain of study, these webs can be large scale or small, and in applied work they are often about people’s multidimensional roles as customers, users, employees, or citizens. Ethnographers look at the social world as dynamically evolving, emergent systems. They are emergent systems because people reflexively respond to the present and past, and this response shapes what they do in the future. Years of building ethnography from this core has generated both analytical techniques and a body of knowledge about sociocultural realities. — https://www.epicpeople.org/data-science-and-ethnography/

→ Research combining quantitative and qualitative methods have been around for a while, of course Aug 29, 2018 highlights & Science Research combining quantitative and qualitative methods have been around for a while, of course. There’s a clichéd logic to mixed methods research–“quant” + “qual”, “hard” + “soft”. EPIC people have broken down assumptions about the quant/qual divide and reframe the relationship between ethnography and big data, but the fact is, mixed methods research combining ethnographic and data science approaches is still rare.2 Some examples are Gray’s (et al.) study of Mechanical Turk workers, Haines’ multidimensional research design, and Hill and Mattu’s investigative journalism, and Bob Evans’ work on PACO — https://www.epicpeople.org/data-science-and-ethnography/

→ The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding Aug 29, 2018 highlights & Science The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding. Data science also offers new opportunities for mixed methods research, for example to generate a multidimensional understanding of human experience both digital/online and offline.
For data scientists, meanwhile, ethnography can offer a richer understanding of data and its provenance, and the sociocultural implications of data science work. As Nate Silver has written, “Numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning…. Before we demand more of our data, we need to demand more of our selves.” There is huge potential when we demand “more of ourselves”, but to realize that potential, people from both fields have to be in the room — https://www.epicpeople.org/data-science-and-ethnography/

→ The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding Aug 28, 2018 highlights & Science The work of data science is increasingly ubiquitous—computational systems are there “in the wild” when ethnographers go into the field, and have consequences for the human experience that is so central to ethnographic understanding. Data science also offers new opportunities for mixed methods research, for example to generate a multidimensional understanding of human experience both digital/online and offline. — https://www.epicpeople.org/data-science-and-ethnography/