#Datafication

[algoworks]

Data is everywhere, and datafication is transforming the way we live and work. From artificial intelligence to machine learning, discover how this powerful technology is unlocking valuable insights and driving innovation, while also posing new risks.

#data #datafication #transformation #artificial intelligence #machine learning #discover #technology #insights #innovation #new risks

[bccunited]

Datafication helps businesses unleash the true power of data and improves your business’ ability to predict strengths, weaknesses, potential, possibilities and outcomes accurately.

For More Visit: https://www.bccunited.com/software/data-analytics-services/

#bccunited #softwaredevelopment #typography #minimaldesign #Datafication #DigitalTransformation #DataDriven #OperationalEfficiency

[trendingtechguruji]

What is Datafication ? How it benefits your business? Definition,Benefits

Datafication refers to the process of transforming various aspects of the world into digital data. It involves capturing, analysing, and representing information from various sources in a structured and machine-readable format. This process enables the quantification and measurement of previously unquantifiable phenomena, such as human behaviour, social interactions, and physical processes. Read More

#datafication #database #technology #structured

[purple-slate]

Datafication — The Future Tense of Data Analytics

La Casa de Papel — Does it ring a bell? Are you familiar with this word? But it is surely in the top 5 of your favorite web series. What? Yes, you may not know the OG Spanish version, but you are a big fan of its English version, Money Heist!

The Spanish version was not a blockbuster. But Netflix translated the show not just into English but also into other languages. The first two seasons went on to become one of the most-watched web series without any promotion or advertisements.

It happened because of recommendation systems that have sophisticated algorithms with the proper tags and classification and user personalization, backed up by data science and machine learning. It is a classic example of datafication.

What is Datafication?

What is datafication — Is that even an acceptable English word? Before, it wasn’t, but it is today.

Datafication refers to the collective tools, technologies, and processes used to transform an organization into a data-driven enterprise. An organizational trend of defining the key to core business operations through a global reliance on data and its related infrastructure.

The crux is, “Datafication” is the process of turning everything into data. It is the act of taking something that was once unquantifiable and turning it into quantitative data.

Datafication enables the transformation of business operations, behaviors, and actions, in addition to those of its clients and consumers, into quantifiable, usable, and actionable data. This information can then be tracked, processed, monitored, analyzed, and utilized to improve an organization and the products and services it offers to customers. To put them into perspective.

Google transforms our searches into data

Facebook transforms our friendships into data

LinkedIn transforms our professional life into data

Netflix or Amazon Prime transforms our watched TV shows and films into data

Tinder transforms our dating activities into data

Amazon transforms our shopping into data

Data either personal or commercial are used to monitor every activity within its reach. Massive datasets are stored that get updated daily by the above tech giants for datafication. Collected data is then used for personalization in the form of ads, push notifications, consumable content, and more within each tech app or platform. This level of interference is usually regulated by the law.

The Datafication of Business

Data has now become a commodity. The currency is data. To produce it, tech companies bring together platform users who create data.

Datafication is a far broader activity, taking all aspects of life and turning them into data format. Once we datafy things, we can transform their purpose and turn the information into new forms of value — Big Data article (2013) by Mayer-Schoenberger and Cukier

Manufacturing and Supply chains

It simplifies the formation of short supply chains, creating micro supply chain business processes condensed through low-cost technologies such as mobile phones.

Real estate

It has made it possible for companies to gain in-depth insights into different locations, which in turn provides a better understanding to business leaders on where is the best place to locate their business.

FinOps

Managing financial activities across an organization is known as financial operations management (FinOps). Datafication is crucial because it enables the analysis and integration of data that was previously isolated in many systems. For example, datafication strives to bring together Accounts Receivable and Accounts Payable systems together to get a single view.

Human resources

Employers can identify potential employees and their unique traits, such as their risk-taking profiles and personalities, using mobile phones, apps, and social network data. Instead of depending on obsolete personality assessments or tests that gauge analytical thinking, it will replace existing exam providers.

Customer relationship management

Many businesses are using datafication to better understand their customers and develop applicable triggers based on their personalities and habits. This information is derived from the vocabulary and tone used in emails, phone calls, and social media.

AIOps

The phrase “AI-as-a-service” (AIOps) is used to describe how AI tools are employed in businesses. Another advanced technology that applies datafication to its domain is this one. Datafication combines a variety of AI tools and is cloud-based to deliver real-time data, insights, and measurements on nearly everything. You can use a web browser or a mobile device to access it.

Benefits of Datafication

Datafication offers enormous opportunities for improving business processes, making it a strategy that is financially advantageous to implement. Datafication is a new developing approach as well as a methodology for building a secure and innovative framework for the future of data analytics.

1. Actionable Insights

Datafication converts unstructured, incomprehensible data into usable insights, allowing you to get insight into your processes and procedures — the basis of any organization.

What do you do well? What needs to be improved? Conversely, what is working well but may be improved? Datafication implies that you will be more capable of understanding your company’s strengths, limitations, potential, and prospects. Also, it provides you with insight into the outcomes and ramifications of your projects, enabling you to assess what you’re doing and how you’re doing it.

2. Digital Transformation

Digital transformation services is no longer a fleeting fad; it is becoming increasingly crucial for all businesses that want to stay up-to-date and pertinent in an ever-changing ecosystem.

To take advantage of the latest and most cutting-edge technologies you should have usable data. It is the ticket to improving business processes and efficiency. It will help you to understand where the organization stands and the required next steps to move forward.

3. Improve Productivity and Efficiency

Datafication will comprehend what you’re doing and how you’re doing it better. Streamlining operations will make better use of all available resources, including employees, to boost overall production and efficiency and, as a result, transform your business into a successful enterprise.

4. Manage Information

Any business is generating a large amount of data and it is being collected and stored every day. If the data is managed well, it shall be providing better results. Otherwise, it can be overwhelming or can become unused data.

Datafication guarantees that you organize it appropriately, allowing you to properly use data to make decisions. You will not only be able to store data but also access and interpret it. Many businesses are experimenting with integrating user-sourced data and incorporating it into apps to contextualize the customer experience.

Conclusion

We know where you are. We know where you’ve been. We can more or less know what you’re thinking about — Erik Schmidt

The concept of datafication may be scary, but properly handled datasets with proper law regulations, security measures, and professional ethics could bring companies to provide customer-friendly and personalized services with the data collected. As datafication becomes more common it is driving innovation, breakthroughs, and betterment for the greater good.

One of the core elements to achieving datafication is by democratizing data access. Ensuring the last line of employees is empowered to access insights can build a data-driven culture that can act as a precursor for setting organizations on the path to datafication. Which brings us to the question, how does one democratize data access?

The shortest answer will be to break the technical barriers surrounding it by introducing language as an interface between data and the user. Or simply engaging in meaningful conversations with data.

Is it possible? With the advancements that have happened around NLP, it’s very much possible. Listen to our webinar on how business intelligence can be reimagined using Conversational AI.

This post was originally published in: https://www.purpleslate.com/datafication-the-future-tense-of-data-analytics/

#Datafication #Data Analytics #Future of Data Analytics #Data Science #Data #Youtube

[itechofficial]

Datafication | The Future of Information Processing

Hello Readers! Today, we will talk about an interesting topic that's been making waves in the tech world – Datafication. Simply put, Datafication refers to the process of turning everything, from everyday objects to human behaviors, into data that can be analyzed and used for various purposes. Read more

#Datafication

[rianmobili]

3. Datafication of The Future is Now: 10 Amazing Technologies!

#top10technology #top10technologyvideo #technology2023 #technologyfuture #Datafication

#top10technology #top10technologyvideo #technology2023 #technologyfuture #Datafication #Youtube

[jobsupdate]

#2022 technology #2022 technology trends #3D Printing #5G #artificial intelligence 2022 #Artificial Intelligence and Machine Learning #best new tech #best tech 2022 #best technology #Blockchain #current technology trends #Cyber Security #Datafication #Digital Trust #Edge Computing #emerging technologies 2022 #Extended Reality #future technology #Genomics #Internet of Things (IoT) #latest technology #latest technology 2022 #latest technology trends #New Energy Solutions #new technology #new technology 2022 #new technology in ai #new technology trends #Robotic Process Automation (RPA) #tech trends

[subhajyotimondal]

The Transformative Power of Datafication in Healthcare

In recent years, the healthcare sector has undergone a revolutionary transformation through datafication — the conversion of diverse healthcare elements into digital data. This shift to a data-driven healthcare ecosystem is reshaping the landscape, enhancing decision-making, personalizing treatments, and improving patient outcomes. In this article, we delve into the significance of datafication in health, its transformative effects, and the benefits it brings to both patients and the industry.

The Rise of Datafication in Health

Healthcare, inherently data-rich, historically grappled with analog and paper-based formats, impeding effective analysis. The digital revolution introduced electronic health records (EHRs) and digital systems, enabling the structured collection, storage, and analysis of health data.

Transforming Health Data into Actionable Insights

Datafication empowers healthcare providers to convert raw health data into actionable insights using advanced analytics and machine learning algorithms. This enables evidence-based decision-making, influencing treatment plans, operational efficiencies, resource allocation, and public health strategies.

Personalized Medicine and Treatment

Datafication facilitates personalized medicine by analyzing individual patient data, tailoring treatment plans based on genetic makeup, lifestyle, and medical history. This approach enhances treatment effectiveness while minimizing side effects.

Predictive Analytics for Disease Prevention

Datafication, through predictive analytics, identifies potential health risks and diseases early by analyzing historical health data. This proactive intervention improves outcomes and reduces healthcare costs.

Benefits of Datafication in Health

The integration of datafication in health yields benefits across patient care, research, innovation, and resource allocation:

Enhanced Patient Care and Outcomes: Real-time monitoring through datafication enables timely interventions, resulting in better medical treatment and improved health outcomes.

Research and Innovation: The vast pool of health data supports research-driven innovations and advancements in healthcare.

Efficient Resource Allocation: Datafication aids in optimizing resource allocation, reducing costs, and increasing operational effectiveness.

The Role of Data in Healthcare

The pivotal role of data in healthcare includes informed decision-making, personalized medicine, research and innovation, healthcare operations and efficiency, healthcare policy and planning, telemedicine and remote monitoring, early disease detection and prevention, quality improvement and outcome monitoring, patient engagement and empowerment, and population health management.

Datafication is reshaping the future of healthcare by harnessing data’s power to drive informed decision-making, improve patient outcomes, and enhance operational efficiencies. As technology advances, embracing datafication becomes crucial in realizing a personalized, efficient healthcare ecosystem focused on delivering the best care possible.

#Datafication in Health

[dailybodh]

Introduction –

Datafication – Data has become a key tool for businesses today, revolutionising fields like accounting and human resources. Despite being a concept that was first used in 2013, datafication is still very important. Datafication, as opposed to digitization, aims to quantify social behavior.

#the future of business in data fluency #datafication whatisdatafication

[fatehbaz]

Goldstein and Mahmoudi point to what, on appearance, is a relatively new phenomenon: namely the use of digital technologies in contemporary forms of surveillance and policing, and the way in which they turn the body into the border. Shoshana Zuboff (2019) has famously referred to the historical moment within which the datafication of human life becomes an industry in its own right as “surveillance capitalism” -- a system based on capturing behavioral data and using it for commercial purposes. According to Zuboff, surveillance capitalism emerged in the early 2000s, with [the major company beginning with letter "G"] as the main driving force [...].

In contrast, scholarship on colonialism, slavery, and plantation capitalism enables us to understand how racial surveillance capitalism has existed since the grid cities of sixteenth-century Spanish Mexico (Mirzoeff 2020). In short, and as Simone Browne (2015, 10) has shown, “surveillance is nothing new to black folks.” [...]

[S]urveillance in the service of racial capitalism has historically aided three interconnected goals: (1) the control of movement of certain -- predominantly racialized -- bodies through means of identification; (2) the control of labor to increase productivity and output; and (3) the generation of knowledge about the colony and its native inhabitants in order to “maintain” the colonies [...].

---

Identification documents and practices can, like so many other surveillance technologies, be traced back to the Middle Passage [...]. [T]he movement of captives was controlled through [...] slave passes, slave patrols, and wanted posters for runaway slaves [...]. Similar strategies of using wanted posters and passes were put in place to control the movement of indentured white laborers from England and Ireland. [...]

Fingerprinting, for example, was developed in India because colonial officials could not tell people apart [...]. In Algeria, the French dominated the colonized population by issuing internal passports, creating internal limits on movement for certain groups, and establishing camps for landless peasants [...]. In South Africa, meanwhile, the movement of the Black population was controlled through the “pass laws”: an internal passport system designed to confine Black South Africans into Bantustans and ensure a steady supply of super-exploitable labor [...].

On the plantation itself, two forms of surveillance emerged -- both with the underlying aim of increasing productivity and output. One was in the form of daily notetaking by plantation and slave owners. [...] Second, [...] a combination of surveillance, accounting, and violence was used to make slave labor in the cotton fields more “efficient.” [...] [S]imilar logics of quotas and surveillance still reverberate in today's labor management systems. Finally, surveillance was also essential to the management of the colonies. It occurred through [...] practices like fingerprinting and the passport [...]. [P]hotographs were used after colonial rebellions, in 1857 in India and in 1865 in Jamaica, to better identify the local population and identify “racial types.” To control different Indian communities deemed criminal and vagrant, the British instituted a system of registration where members of particular tribes were not allowed to sleep away from their villages without prior permission [...].

---

In sum, when thinking about so-called surveillance capitalism today, it is essential to recognize that the logics that underpin these technologies are not new, but were developed and tested in the management of racialized minorities during the colonial era with a similar end goal, namely to control, order, and undermine the poor, colonized, enslaved, and indentured; to create a vulnerable and super-exploitable workforce; and to increase efficiency in production and foster accumulation. Consequently, while the (digital) technologies used for surveillance might have changed, the logics underpinning them have not.

---

Text by: Sabrina Axster and Ida Danewid. From an article by Sabrina Axster, Ida Danewid, Asher Goldstein, Matt Mahmoudi, Cemal Burak Tansel, and Lauren Wilcox. "Colonial Lives of the Carceral Archipelago: Rethinking the Neoliberal Security State". International Political Sociology Volume 15, Issue 3, pp. 415-439. September 2021. [Bold emphasis and some paragraph breaks/contractions added by me.]

#abolition #landscape #colonial #imperial #ecology

[randomidiocyncrazies]

thinking about Appmon’s finale again... tbh i always thought there’d be a small but vocal group of people who are extremely anti-tech in the wake of the (failed) Human Application Project?

Leviathan’s takeover was horrifying on an existential level, and even if people don’t remember being controlled and trapped in Leviathan’s monochrome world, the lead up to the datafication is out of a horror movie—i really think there are groups of people who’d go entirely off the grid and advocate for shunning AIs & technology due to the experience (and we just don’t see them appear on screen because it doesn’t fit Appmon’s theme of co-existence with technology).

which is to say... i think there’s a very high chance of Yuujin’s other friends and classmates, like Watson and co., thinking that Yuujin had been kidnapped by his mom to go live in one of those anti-tech communes in the aftermath of Leviathan’s attack—he and his mom just disappeared immediately afterwards, no one can get in touch with him, but Haru and Ai seem very sure they’d see him again some day... from an outside perspective, Yuujin being whisked off to an anti-tech life is probably the most convincing explanation for all of the above.

tbh, a part of me thinks Haru and Ai’s casual unwavering certainty of seeing Yuujin again freaks the other kids out a bit; they probably don’t think Yuujin is dead or anything—unless there are cases of people dying from the event, which i don’t think happened (Leviathan’s goal is to prevent death and fear of death)—but they do think Haru and Ai are coping really badly and maybe borderline delusional?

(on that note, i absolutely think Yuujin’s “mom” ran after the Project failed—it’s possible that she stayed to help rebuild or whatever, but given her involvement with L-Corp, she’d be in a lot of legal hot water. and if she stayed in the community, she’d have to come up with a reason for why her “son” isn’t around anymore... unless of course she goes public with Yuujin’s real identity, but there’s nothing that really suggests that the public knows about androids collecting human data in their midst in the epilogue? and a reveal like that would heighten anti-tech sentiment)

all of this is a very, very long-winded way to say i kinda love the idea of Watson running into a resurrected Yuujin years down the line when waiting for the subway or whatever, and trying to figure out the mystery of what happened to Yuujin back then. I don’t think he succeeds in getting the real story, but Watson is at heart a nosy busybody (affectionate).

((and then Yuujin derails the investigation by casually mentioning that he’s married/engaged to Haru now

watson: huh????? Wait that makes sense actually, now that i think about it... congrats!))

#appmon #appli monsters #digimon universe appli monsters #appmon spoilers #harujin

[fundgruber]

As can be observed with the Semantic Web, where information is more useful if it is expressly linked to other information, associating new knowledge with existing knowledge in a data infrastructure may present researchers with a transformative tool rather than the paginated metaphor of the Web. The linking of snippets of knowledge, or as semantic assertions via a browsable (knowledge) graph, to form insight, understanding and new knowledge, can have both advantages (trust through association) and disadvantages (incorrect assertions).

Jennifer Edmond , Nicola Horsley , Jörg Lehmann and Mike Priddy: The Trouble With Big Data: How Datafication Displaces Cultural Practices. Bloomsbury. 2022 p. 65

#Medienkompetenz #semantic web

[transmutationisms]

thoughts on bibliometrics and like quantifying research ‘impact’? is it actually useful

entirely depends what purpose you're talking about! a paper's impact score or citation metrics don't, on their face, tell you anything about the quality of the research or how insightful the claims made are. the process of knowledge dissemination and information transfer is a huge knotty sociological problem, including in the sciences; i do think bibliometrics can sometimes rely on (& perpetuate) the idea that information will simply be adopted in direct relation to how 'useful' it is, and that's a dangerous assumption to make. on the other hand, though, from a historiographical perspective bibliometrics are often a fascinating data source and it's a continual frustration for those of us who work with sources that predate this type of 'datafication' that we have to rely on guesswork or proxy indicators to figure out how widely read, cited, or disseminated a text or claim actually was.

[mariacallous]

You're not being paranoid. If you always feel like somebody's watching you, as the song goes, you're probably right. Especially if you're at work.

Over the course of the Covid-19 pandemic, as labor shifted to work-from-home, a huge number of US employers ramped up the use of surveillance software to track employees. The research firm Gartner says 60 percent of large employers have deployed such monitoring software—it doubled during the pandemic—and will likely hit 70 percent in the next few years.

That's right—even as we've shifted toward a hybrid model with many workers returning to offices, different methods of employee surveillance (dubbed "bossware" by some) aren't going away; it's here to stay and could get much more invasive. 

As detailed in the book Your Boss Is an Algorithm, authors Antonio Aloisi and Valerio de Stefano describe "expanded managerial powers" that companies have put into place over the pandemic. This includes the adoption of more tools, including software and hardware, to track worker productivity, their day-to-day activities and movements, computer and mobile phone keystrokes, and even their health statuses. 

This can be called "datafication" or "informatisation," according to the book, or "the practice by which every movement, either offline or online, is traced, revised and stored as necessary, for statistical, financial, commercial and electoral purposes."

Ironically, experts point out that there's not sufficient data to support the idea that all this data collection and employee monitoring actually increases productivity. But as the use of surveillance tech continues, workers should understand how they might be surveilled and what, if anything, they can do about it.

What Kind of Monitoring Is Happening?

Using surveillance tools to monitor employees is not new. Many workplaces continue to deploy low-tech tools like security cameras, as well as more intrusive ones, like content filters that flag content in emails and voicemails or unusual activity on work computers and devices. The workplace maxim has long been that if you're in the office and/or using office phones or laptops, then you should never assume any activity or conversation you have is private.

But the newer generation of tools goes beyond that kind of surveillance to include monitoring through wearables, office furniture, cameras that track body and eye movement, AI-driven software that can hire as well as issue work assignments and reprimands automatically, and even biometric data collection through health apps or microchips implanted inside the body of employees.

Some of these methods can be used to track where employees are, what they’re doing at any given moment, what their body temperature is, and what they’re viewing online. Employers can collect data and use it to score workers on their individual productivity or to track data trends across an entire workforce.

These tools aren't being rolled out only in office spaces, but in work-from-home spaces and on the road to mobile workers such as long-haul truck drivers and Amazon warehouse workers.

Is This Legal?

As you might imagine, the laws of the land have had a hard time keeping up with the quick pace of these new tools. In most countries, there are no laws specifically forbidding employers from, say, video-monitoring their workforce, except in places where employees should have a “reasonable expectation of privacy,” such as bathrooms or locker rooms.

In the US, the 1986 Electronic Communications Privacy Act laid out the rule that employees should not intercept employee communication, but its exceptions—that they can be intercepted to protect the privacy and rights of the employer or if business duties require it, or if the employee granted prior permission—make the law toothless and easy to get around.

A few states in the US require employers to post notice if they are electronically monitoring people in the office, and there are some protections for the purpose of collective bargaining, such as discussing unionizing.

In February, US Democratic senators led by Bob Casey of Pennsylvania moved to introduce legislation to curtail workplace monitoring by employers. It would require bosses to better notify employees of on- and off-duty surveillance and would establish an office at the US Department of Labor to track work monitoring issues.

What You Can Do

Privacy experts say that unfortunately for many employees, the only recourse for a worker who doesn't like a company's surveillance policies is to find another job.

Short of that, employees can make a formal request for disclosure of a company's data collection and surveillance policies, typically from the human resources department. Such policies may be outlined in an employee handbook, but also may not be readily available, especially for smaller companies and startups. Workers who are part of a workers' union can request the information through their representatives.

A company may not know it is required to post that it's surveilling employees or that it is in a state where two parties must consent to phone-conversation monitoring. You could choose to let your company know it's not in compliance, and if the company doesn't make changes (and you’re in the United States), you could alert your state's workforce commission or file a complaint with the US Occupational Safety and Health Administration or over HIPAA (Health Insurance Portability and Accountability Act) medical privacy issues.

Apart from all that, general data hygiene is also a good counter. Clear your browser cache regularly, and don't keep private data on work devices or transmit them over work email accounts. Block your workstation's webcam when it's not in use (if you're allowed to do that) and ask your employers if you can opt out of surveillance tools that are not required for your work.

Most importantly, be mindful when your employer issues notices about workplace privacy changes or when new software or hardware is introduced for the purposes of monitoring. Ask questions and research what these tools are if you don't get a good explanation from your bosses.

[jurisffiction]

when the day comes and techbro colonialist 4chan/reddit datafication overlaps with tumblr/tiktok compulsive pathologising while accelerating far past the reach or concern of ethics and finally AI can and will psychiatrically diagnose anyone off any large enough speech sample i will be there feeding it my own blog til it chokes 

#no punctuation you decide if i paused anywhere or not

[robhorninginternalexile]

From Intellectual and Manual Labor (1977) by Alfred Sohn-Rethel. 

This book is useful for situating “AI” as an extension of earlier forms of automation. Much of it is given over to a critique of ahistorical epistemology, but the quote from Marx above and Sohn-Rethel’s gloss on it gets to the heart of the matter: Automation is not driven by new capabilities of new technology but by the demands of capitalism, which requires that workers be deskilled and disciplined. Under capitalism, machinery (and technology) must serve that purpose, and modern science is intrinsically oriented toward it. 

As Sohn-Rethel puts it, “The postulate of automatism as a condition for the capital control over production is even more vital than its economic profitability — it is fundamental to capitalism from the outset ... The capitalist control over the labour process of production can only operate to the degree to which the postulate of automatism functions. The stages in the development of capitalism can be seen as so many steps in the pursuit of that postulate, and it is from this angle that we can understand the historical necessity of modern science as well as the peculiarity of its logical and methodological formation.” He points to mathematicization — what we might call datafication — as the main form this science takes. 

AI, then, appears as the current expression of that peculiar methodology, the necessary form of “bourgeois science.” It seems self-evident that generative models and the data they run on are only the latest process for allowing “dead labor” to “soak up living labor-power” and turn it into a form of domination exercised over workers, conditioning their work processes as an increased form of dependency on capital. 

Another way to put that — the idea that is implicit in every sci-fi depiction of rogue machines subordinating humanity — is that capital itself is always an “artificial intelligence” that emerges from the concentration of economic power:   

capital is a social power which takes over production where it has outgrown the economic and technological capacities of the direct producer controlling it himself. While in the economic field the social power is capital, in the field of technology it is science, or, more accurately, the methodical operation of the human mind in its socialized form, guided by its specific logic, which is mathematics.

Marx continues the passage Sohn-Rethel quotes with this: “The special skill of each individual insignificant factory operative vanishes as an infinitesimal quantity before the science, the gigantic physical forces, and the mass of labour that are embodied in the factory mechanism and, together with that mechanism, constitute the power of the ‘master.’"

It is easy to translate this into contemporary terms: Workers vanish into data, which the gigantic tech companies’s computer scientists have assembled into models that serve as factories of a sort, but only insofar as they dictate the only viable form of labor capitalists will deign to offer, forms that reinscribe workers’ infinitesimal insignificance.