Shore says that nonprofits can and should be more political than many in the nonprofit community believe they are legally permitted to be. Achieving the goals that many nonprofits pursue depends upon nonprofits becoming more, not less, political. While some activities are prohibited, including working on campaigns, donating to candidates, and engaging in lobbying beyond certain generously defined limits, shore notes that “… a broad range of political work is permitted, appropriate, even essential. (There is also the option of establishing a 501(c)(4) that permits campaign engagement and support, which we haven’t done.)”
Shore asserts getting political is often about educating, not necessarily lobbying or campaigning. He further argues that, “Nonprofits need to build their internal political capacity. Nonprofit political activity is good for nonprofits, good for politics, and good for the people that both aim to serve.” Ultimately, by expanding their political activities within stipulated legal limits, “nonprofits benefit by seeing their programs and services achieve greater scale and reach more people in need, in ways that only politics and public policy can guarantee.”
“Getting Political Is Good for Everyone,” Bill Shore, Stanford Social Innovation Review, July 17, 2019
In “A Machine May Not Take Your Job, but One Could Become Your Boss,” by Kevin Roose, New York Times, June 23, 2019, the author says “…in all of the worry about the potential of artificial intelligence to replace rank-and-file workers, we may have overlooked the possibility it will replace the bosses, too.” Roose observes that one of the goals of AI is to optimize efficiency of humans in the workplace. Thus systems that monitor, guide, and report on employee performance are increasingly seen in white collar workplaces, where employees are being “assisted” to be more productive, more customer friendly, and work more quickly — by “adjunct management,” i.e., artificial intelligence.
Roose scans a variety of workplaces. In the insurance industry, he reports on AI systems that provide on-screen prompts to call center works — prompting them to be chirpier and more empathetic; he discusses the use of AI and employee tracking in both Amazon warehouses and retail stores. In the later he notes that 7-Eleven “uses in-store sensors to calculate a “true productivity” score for each worker, and rank workers from most to least productive,” and notes “Uber, Lyft and other on-demand platforms have made billions of dollars by outsourcing conventional tasks of human resources — scheduling, payroll, performance reviews — to computers.
Management by algorithm doesn’t just affect call center works, Uber drivers, and warehouse workers. It also bodes less-than-well for managers, whose traditional supervisory and over-site duties are increasingly being handled by “robots”. As we discussed in “Humans Need Not Apply,” AI promises to displace both blue collar, manual laborers and white collar, college-educated professionals — the latter including but not limited to, lawyers, computer programmers, managers, office and retail workers. “A Machine May Not Take Your Job, but One Could Become Your Boss,” hauntingly suggests that management too, is in the cross-hairs of AI.
“Robots Are Not Coming for Your Job. Management Is,” Brian Merchant, Gizmodo
In an important and brief new book, The Metric Society: on the Quantification of the Social, (Polity, 2019), German sociologist Steffen Mau argues that the historic growth in the availability of data and a seeming societal obsession with quantitatively measuring and ranking everything, is fast making us a “metric society. “A cult of numbers masquerading as rationalization” he says, is having unparalleled impact on how we understand both social and personal value. We are becoming increasingly trapped in a social world where, “The possibilities of life and activity logging are growing apace: consumption patterns, financial transactions, mobility profiles, friendship networks, states of health, education activities, work output, etc.—all this is becoming statistically quantifiable.” Such quantification is far from neutral and scientific, Mau says. It leads to ever greater tendencies, both individual and institutional, to classify, differentiate, and construct social hierarchies. He argues further that these tendencies are paving the way for us to become “an evaluation society,” a society where individuals constantly measure and compare their social worth with others (e.g., dating sites and Facebook “likes”) and where both corporations and the state sort people, based on narrow statistics, into categories that ultimately have differential access to valuable resources.
While the book is filled with examples, Chapter 5, “The Evaluation Cult: Points and Stars,” explores how “‘the evaluation cult’ is binding us to the metrics of measurement, evaluation, and comparison.” Mau scans the proliferation of various tools for evaluation: satisfaction surveys, preference measures, self-assessments, health tracking algorithms, and myriad ranking systems, ranging from Yelp, to publicly available starred reviews of medical providers and lawyers. He shows us how such ratings and rankings—often justified by the claims of providing “transparency,” helpful information, and consumer influence on service providers and products— are upending both markets and the professions, in some cases driving companies to purchase good reviews.
Mau raises questions not just about the validity of measures (after all, what is the difference between a three-star restaurant rating and a four-star rating?), but argues that the growth in the use of such measures is transforming how we view and value ourselves and others. “The universal language of numbers, their lack of ambiguity, and the illusion of commensurability, pave the way for the hegemony of a metrics-based apparatus of comparison.” He says that today, we are witnessing and participating in the emergence of a new “status regime” characterized by quantification and numerical ranking. This “quantitative comparison is frequently translated into a competitive ethos of better versus worse, more versus less.”
Among the other observations Mau offers:
- growing reliance upon numbers changes our everyday notions of value and social status
- the availability of quantitative information reinforces the tendency toward social comparison and rivalry
- quantitative measurement of social phenomena fosters the expansion of competition
- representations of quantitative data, such as graphs, tables, lists, and scores, change qualitative differences into quantitative inequalities
- the availability of, and reliance upon, quantitative data leads to further social hierarchization
Ultimately, “…the measurement and quantification of the social realm are not neutral representations of reality. On the contrary, they are representative of specific orders of worth which are invariably based on forgone conclusions as to what can and should be measured and evaluated, and by what means. Metrics may claim to give an objective, accurate, and rational picture of the world as it is, but they also contribute, through the selection, weighting, and linking of information, to the establishment of the normative order.” Essentially. Mau raises a perennial question that is relevant to all evaluative efforts: Do we measure what’s valuable, or is it valuable because we choose to measure it? Please see our previous posts “The Tyranny of Metrics” and “What Counts as an ‘Outcome’—and Who Determines?” Mau argues further that we are becoming a society obsessed with managing our reputations, and ultimately a society of ever greater competition and rivalry.
The Metric Society: On the Quantification of the Social, (Polity, 2019),
Heather Douglas, “Facts, Values, and Objectivity”
Max Weber, Objectivity in the Social Sciences
Max Weber, Methodology of the Social Sciences, Transaction Press, 2011
In a previous blogpost, “Interpersonal Skills Enhance Program Evaluation,” we discussed the importance of interpersonal and relational skills for program evaluators. These skills make effective and responsive interpersonal interaction possible. “Emotional Intelligence” underlies many of these skills. Emotional Intelligence, first explored by Daniel Goleman, in his book Emotional Intelligence, Why It Matters More Than IQ, Bantam Books, 1995 is the ability to recognize, manage, and utilize both one’s own emotions and the emotions of others. Emotional Intelligence, as summarized by Eric Ravenscraft in his recent article “Emotional Intelligence: The Social Skills You Weren’t Taught in School,” Lifehacker, February 20, 2019, includes the following elements:
- Self-awareness: Self-awareness involves knowing your own feelings. This includes having an accurate assessment of what you’re capable of, when you need help, and what your emotional triggers are.
- Self-management: This involves being able to keep your emotions in check when they become disruptive. Self-management involves being able to control outbursts, calmly discussing disagreements, and avoiding activities that undermine you like extended self-pity or panic.
- Motivation: Everyone is motivated to action by rewards like money or status. Goleman’s model, however, refers to motivation for the sake of personal joy, curiosity, or the satisfaction of being productive.
- Empathy: While the three previous categories refer to a person’s internal emotions, this one deals with the emotions of others. Empathy is the skill and practice of reading the emotions of others and responding appropriately.
- Social skills: This category involves the application of empathy as well as negotiating the needs of others with your own. This can include finding common ground with others, managing others in a work environment, and being persuasive.
Although Emotional Intelligence (EI) has become an increasingly accepted concept, there are some who question its distinctiveness and validity. Some say that it is difficult to distinguish from regular IQ, that is not really a kind of intelligence but a set of behaviors, and that it is nearly impossible to objectively measure.
A recent article argues that the idea of “reading” the emotions of oneself and of others is itself a problematic conception. In “Emotional Intelligence Needs a Rewrite”, Lisa Feldman Barrett, Nautilus, August 3, 2017, writes that “The traditional foundation of Emotional Intelligence rests on two common-sense assumptions. The first is that it’s possible to detect the emotions of other people accurately. That is, the human face and body are said to broadcast happiness, sadness, anger, fear, and other emotions, and if you observe closely enough, you can read these emotions like words on a page. The second assumption is that emotions are automatically triggered by events in the world, and you can learn to control them through rationality.”
The author offers an alternative, neuroscientific view of how the brain works. She says that our brains “create all thoughts, emotions, and perceptions, automatically and on the fly, as needed. This process is completely unconscious. It may seem like you have reflex-like emotional reactions and effortlessly detect emotions in other people, but under the hood, your brain is doing something else entirely.” Essentially our brains are survival-oriented prediction engines, that produce responses to internal and external stimuli that “become the emotions we experience and the expressions we perceive in other people.” Therefore “Emotional Intelligence requires a brain that can use prediction to manufacture a large, flexible array of different emotions. If you’re in a tricky situation that has called for emotion in the past, your brain will oblige by constructing the emotion that works best.”
Feldman Barrett argues that we don’t so much observe emotions in ourselves and others, as we construct them and predict them. She further argues that we can give our “constructivist” brains (and their concomitant emotions) a boost by enhancing the granularity of our sensitivity to our feelings and emotional states. One way we can do this is by learning greater vocabularies to describe our own and other’s feeling states, and thereby priming our prediction engines to “guess” what others are feeling, with even more specificity.
Whether our brains construct emotions and predict the emotions of others seems largely irrelevant to the issue of the importance of understanding emotions in ways that help us relate to, and interact with, others. In the final analysis, the human social world is composed by thinking and feeling beings, and those who can understand (“predict” in Feldman Barrett’s view) and manage emotions will be better prepared to engage in that world.
Daniel Goleman, Emotional Intelligence, Why It Matters More Than IQ. Bantam Books, 1995.
Emotional Intelligence: “The Social Skills You Weren’t Taught in School” Eric Ravenscraft. Lifehacker, February 20, 2019
“What is Emotional Intelligence” Michael Akers & Grover Porter, Oct 8, 2018 Psych Central
“Emotional Intelligence,” Wikipedia
It’s sometimes difficult to tell the truth, especially in arenas like the workplace, where inequalities of power and authority make it difficult to “speak truth to power.” In a recent Harvard Business Review article, “4 Ways Lying Becomes the Norm at a Company” (February 15, 2019) Ron Carucci discusses the results of a substantial, 15 year longitudinal study that examined the systemic (vs. personal) incentives for dishonesty. Carucci says there are a range of incentives, or prompts, for employees to be less than honest at work. Among these:
- Inconsistency: An inconsistency between an organization’s stated mission, objectives, and values, and the way it is actually experienced by employees and the marketplace. As one interviewee put it, “Our priorities change by the week. Nobody wants to admit we’re in trouble, so we’re grasping at straws. We don’t know who we are anymore, so we’re just making things up.”
- Unjust accountability systems, especially when an organization’s processes for measuring employee contributions is perceived as unfair or unjust. Research shows that people are nearly 4 times more likely to withhold or distort information when the system is perceived to be unfair or rigged.
- Poor organizational governance; for example there is no effective process to gather decision makers into honest conversations about tough issues. Truth is forced underground, leaving the organization to rely on rumors and gossip.
- Inter-group rivalry, conflict, and competition (what Carlucci terms “weak cross-functional collaboration.”) is a predictor of people withholding information or distorting truthful information. Additionally, Carlucci observes that isolation, fragmentation, and departmental/divisional loyalties often result in dishonesty or a damaging lack of candor.
Because these factors are cumulative, an organization afflicted with all four of these factors is 15 times more likely to end up in an “integrity catastrophe” than those who have none of these four integrity/honesty problems. Carlucci argues however, that these organizational problems are alterable and that a culture of honesty can be achieved by companies and organizations that challenge these issues.
“4 Ways Lying Becomes the Norm at a Company,” Ron Carucci, Harvard Business ReviewFebruary 15, 2019