Pandemic life and productivity

Are we “post-pandemic”? The epidemiological data say we are most definitely not past the pandemic. Yet, most of are already experiencing a “post pandemic” way of working. What does work after the pandemic look like?

A few weak signals to consider:

 

Why I decided to self-publish my second book

You know it’s true what they say — the second baby is much more of a blur than the first. My good friend Carol likes to tell the story about giving birth the second time with her cardigan still buttoned up. There she was, in the delivery room, bundled from the waist up. When the baby comes, it comes.

And by baby, I mean book. Yes, my second book took me a little by surprise too. Sure, I had enough time to make a cup of tea and change into slippers (sorry, Carol) but I didn’t have the time for a publisher.

Way back in 2012, I began writing my first book. I had secured what was known as a “book deal” with a small social science publisher that had a good reputation among people I knew professionally. I knew nothing about publishing, of course, except that someone would put my ink on some sort of pages and paste them all together. Or would they sew them together? That’s a thing, isn’t it? Given my total ignorance, I thought it was acceptable to earn only 8% of every book sold. That’s right 8%.

I still earn only 8% on Practical Ethnography.

I fought harder for the 15% I now earn on all Kindle copies of Practical Ethnography but I thought it was worth it to learn how to publish a book. In retrospect, I don’t know if it was. The publisher hired a copyeditor for me, but I found we mostly argued about emdashes and doubt quotes instead of making my actual copy better. The design of the book was…well it was okay. The marketing was abysmal. I had the good sense to buy the domain name myself, and several years after publication, when my little West Coast publisher got purchased by British Behemoth, I told myself I never wanted all that hassle again.

So when my second “baby” was ready to arrive, of course I tuned to my friend Carol for help! Luckily, Carol is also a professional journalist and has a cadre of colleagues who are fantastic freelance copyeditors. Her old buddy Rebecca came on board and managed to midwife this book so quickly that my tea never even got cold. But then what? Do I sew together the pages like before? The prospect of a print book almost sent me to another publisher. Almost.

I bit the bullet and learned how to make a print book.  I got on Reedsy.com. I hired a designer. I hired a web designer. I took the cover photo myself. I released it on Kindle to finance the whole shebang. And now, here we are. Book Number 2 is now available in paperback and Kindle, and it’s a 100% Sam Ladner Production. Special thanks to Carol, Rebecca, book designer Sarah Beaudin and web designer Derek Moore. It really does take a village to raise a child.

I’m a proud momma. Please check out my beautiful baby on her own beautiful web site.

Mixed Methods Guide Web Site Screenshot

New book alert: applied mixed methods research

My latest book is now available on Kindle! The book is a short guide to doing applied mixed-methods research. It’s ideal for people who work in applied research role in industry, but may not have a broad, methodological training in both qual and quant research methods.

What’s inside:

Table of Contents
Introduction: Understanding The Qual/Quant Divide
A Mixed Methods Example: Stories as a Network
What Are Mixed Methods? Mixing Objectivism and Constructivism
    • A constructivist view of technology
    • How the qual/quant divide plays out
    • Abandoning the scientific method: the creative encounter
    • Mitigating the weaknesses of qual and quant research

Why mix methods?

Ways to mix methods

How to mix methods, step by step

    • Project kick-off and framing
    • Developing shared understanding about research needs and resource constraints
    • Framing your approach: inductive or deductive frame?
    • Social Infrastructure: preparing teams for mixed methods
    • Research design
    • Priority or dominance
    • Data collection: simultaneous or sequential
    • Sequential designs
    • Simultaneous designs
    • Clarifying concepts or variables
    • Data analysis and interpretation: mixing Induction and Deduction
    • Can analysis and interpretation be a team sport?
    • During reporting

New Horizons for Mixed Methods Research

Conclusion: To Mix or Not to Mix?

References

What even IS this? Why tech companies are still failing us

Why do we know so little about the social implications of technology? It plays a starring role in everyday life, as essential as food, shelter, and clothing. A huge share (70%) of Americans use social media, and even 65% of senior citizens use Facebook  – that’s more than the number of people who eat family dinner at home, attend church, or have a pet.

Yet, we know so little about technology’s impact on everyday life. We are only just now recognizing problems like coordinated disinformation, breaches of personal data, and algorithmic discrimination.  Clearly, technology companies are falling short on understanding the social implications of their tools before and after they build them. But why? Why are tech companies failing us?

Woman in kitchen.
Woman in kitchen. Source: Art Institute of Chicago

Sadly, it is all too predictable that technologists underestimate, misjudge, or otherwise underappreciate how humans will interact with their technology. This is for one simple reason: engineering, as a discipline, does not bother to ask:  “What is this?”

Engineers are not scientists, much less social scientists. They typically have no knowledge of basic human behavior such as loss aversion or impression management, even though these are the building blocks of social interaction – and entry-level knowledge for social scientists.

Engineers could ask, “What is this?” but instead choose to ask: “Does this work?”

“Does this work?” underpins research within tech companies. Once upon a time, tech companies hired engineers they called research scientists and stuck them in labs to tinker endlessly with pieces of hardware and scraps of computer code. Even today, there are over 7800 job postings for “research scientist” on LinkedIn, which are typically engineers or computer scientists. A posting for an Uber research scientist intern is instructive. In addition to having a Master’s degree in a “technical field,” the intern is also encouraged to engage in “risk taking” and to “turn the dreams of science fiction into reality.”  Another job posting for a research scientist at Facebook asks for skills in the scientific method, but then specifically narrows that down to “evaluate performance and de-bug.” In other words: Does this work? Notably not mentioned: the ability to develop basic knowledge.

Academics would see much of this activity as more akin to prototyping than to scientific inquiry. Indeed, these engineers produced many technology prototypes, but not much in the way of generally applicable knowledge, or what the rest of us might call “science.” In other words, they never seem to stop and ask, “What is this?”

Painting by Salvador Dali
Inventions of The Monsters

Today, tech companies need to ask things like “What is a digital public sphere?” and “What is the nature of privacy?” and “What is artificial intelligence versus human intelligence?” Tech companies need typologies of human-computer interactions, motivations, fears, and human foibles. They need to create a system of knowledge around key questions of technology like artificial intelligence and social media.

Some argue that technology development doesn’t have time for “understanding,” that asking “What is this” takes too long and is too expensive. But this is a false economy. Philosopher Martha Nussbaum tells us plainly that we need that understanding, not for understanding’s sake but because it guides our planning:

“Understanding is always practical, since without it action is bound to be unfocused and ad hoc.” — Martha Nussbaum

In other words, if you don’t know “What is this” you’re probably going to build the wrong thing.

We can see this pattern of building the wrong thing in technology, over and over again. The term “user friendlywas invented way back in 1972. Curiously, “user hostile” wasn’t invented until 1996, just before Microsoft’s infamous Clippy appeared in 1997. Clippy’s abrupt entre onto the desktops of the world indicated that technology “researchers” had no idea what they had made. Word famously exploded from what appeared to be a digital typewriter, to a swollen behemoth that did everything from create a newsletter to automate mailing labels. Pick a lane, people. Clippy was there to tell users how to make Microsoft Word work, but no one bothered to find out much less explain what Microsoft Word actually was.  Word is still so swollen that a new user today can credibly ask “What even IS this?”

Clippy the paperclip
Source: NYMag.com

Flash forward to today, and the so-called “lean startup” approach to building technology is really just a faster, even more facile way to ask “Does this work.” In reality, tech companies still don’t know, “What is this?” even after they’ve built a working prototype.

In my former role as a hiring manager at a major tech company, it took an average of 100 days to hire just one ethnographer and more often than not, the job remained open much longer than that. These are the very people who can tell us, “What is this?” The demand for these social scientists only grows. Yet, the tech industry as a whole has not yet figured out they need to ask “What is this?” before they build something.

Were tech companies to ask, “what is this,” they would learn the basic properties of their tools, their coherence, intelligibility, performance, and affordances. Instead, they are fully occupied with “does this work,” and create horrific blights on our collective consciousness like Tay, the racist AI Bot on the relatively innocuous end of the scale, and Compass, the racist parole algorithm at the full-on evil end of the scale.

Technologists do not know what they do not know. Ethnographers hope for the day when they can just ask “What is this” without worrying about whether it works, because it doesn’t even exist yet. But tech development continues apace.

It’s time for ethnographers to stop this sad venture, and instead insist on asking: What IS this? Before another Tay, before another Compass. Technologists too must take responsibility because if we don’t, the 21st century will become even more technocentric, and even less intelligible. Let’s find out what’s going on before we build anything else.