strategy Archives - Learnlets

Generalists and Specialists

25 August 2026 by Clark Leave a Comment

It’s always been a battle between generalists and specialists. Despite folks like Don Norman making the case why we need both, we’re seeing this play out again. A post by Tom McDowell triggered this recognition, so let me talk a wee bit about what I see going on.

Tom’s post was about how you shouldn’t automate a process until you understand it. The issue is that until you map it, you can’t say you truly comprehend it. Further, if you don’t comprehend it, you can’t decide what the real tradeoffs are in automating it. This triggered a realization in me.

So, I’ve argued in the past that we shouldn’t digitize processes until we’ve looked at them. If you automate before reviewing them, you merely solidify your existing paths. Which, if like the Humanities & Social Sciences building at UCSD, you’ll find that people cut paths cross the grass, not stay on the concrete. That’s because the design wasn’t considering human behavior. (I think this example was in one of Don’s books; I can’t find which, but I lived it.) Digitization instantiates your processes, so make sure your processes are good first!

Which brings me to the current situation. Large Language Models (LLM) are in use more broadly than their real affordances. The underlying technology is more general, but these have been optimized for language, that is to make good sounding language. And they’ve been trained (illegally) on a vast corporate of (English) language. Consequently, they make very convincing statements about the world, whether correct or not. They make it easy to do more tasks than just language, so people do that. There are consequences.

For one, we’re losing the development path. Doing low-level work established the foundations for high-level work. Er, if you’re human. If you don’t get that indoctrination (not hazing), you don’t become an expert. Outsourcing that work means you don’t have a way for your new workers to develop the necessary expertise, which will walk out the door. And you can’t just hire experts. Knowing what you can outsource, then, becomes critical.

Another, of course, is that outsourcing the wrong things can cause trouble. We have seen bad processes reinstantiated in generated content, instead of actual learning experiences. I’m sure you’ve had the dubious pleasure of interacting with a chatbot instead of customer service (there can be good ones, but it takes a fair bit of work to make it so, contrary to the claims). Then, even if you have oversight and responsibility, you can fatigue if there are expectations to oversee something that’s mostly right. You learn to trust it, despite a conscious awareness that’s wrong.

The problem that struck me is that it’s likely the case that folks are over-trusting these generalist systems that seem intelligent. (They’re really just probabilistic engines, but to some extent so are we.) They do have lots of knowledge, but it’s context free, and trying to get them to understand context is what is known as prompt engineering (a term that’s disappearing). Still, it seems like a quick and cheap fix to get these generalist engines to do specialist tasks. I’ve been arguing that rather it makes sense to make specialist agents. The recognition came that the reason is because we really don’t have AGI (artificial general intelligence) yet (and we’re a long way from doing that).

So, yes, I have some bias. I’ve been and am pro-AI, but I don’t like the way the big companies have built their systems, they’re overhyped, and the costs are yet to be seen. Take all this with the proverbial boulder of salt. Still, I reckon there are better alternatives, at least in the long run. That is, small agents you build and train, so that you know what they can, and can’t, do. Little systems are cost-effective and more secure, too. It’s being smart about sensibly distributing roles for generalists and specialists across technology. That’s where my thinking (and recommendations) are going. What are your thoughts?

AI Slop and Brain Rot

18 August 2026 by Clark Leave a Comment

I’ve been complaining about AI slop, probably not publicly, but clearly. And, I’ve been noticing the complaints about brain rot from using AI. Let’s be clear, this is an instance of people saying “AI” and meaning generative artificial intelligence, a subset. Still, both are being seen, and not surprisingly, they’re linked. At least, let me make the case that AI slop and brain rot are related.

AI slop, to be clear, is when folks are using AI to generate content. It can be passive, as that awful image that had me as one of the ‘wealthy influential L&D leaders’. Wealthy? As if (as in, in my dreams). Sorry, my bad habit of ‘standing up‘ for what I believe gets in the way, way too much! AI slop can also be interactive, such as the chatbots (textual or auditory) that are replacing people. In either case, it’s using an average of stolen intelligence to create responses, and that’s only minimally worthwhile even if, as Markus Bernhardt reminds us, we provide oversight. Which doesn’t happen, because it’s too cheap to just have AI create worthless content.

There is also the phenomena of brain rot, where people who use AI, unless using it well (which is rare) get dumber. If you outsource the mental challenge, you aren’t doing the work that maintains and builds mental capacity. We’re increasingly seeing evidence that folks who outsource to AI end up diminishing their capability. Unless they’re using it as an idea partner and a form of feedback – generating their own ideas and using the AI output as a check – they’re setting themselves up for failure. It’s like with your muscles, use ’em or lose ’em. Same with your brain.

Similarly, companies that let AI handle low-level tasks are breaking up the pathway to expertise. Sure, it’s a short-term win, but…what happens when the expertise retires, and you’ve no one who’s been developing to replace? Maybe AI will get good enough to handle that. But there’s also evidence that the technology is reaching the law of diminishing returns. And, what with the move to the market, the costs are likely to go up. Finally, the tech is optimal for language tasks; using it to do other things, such as numeric tasks, is a quick trip to inaccuracies. There are better approaches.

At core, my point is that the source of the problem is the same: the technology isn’t being used in ways consonant with it’s strengths. (Let alone in ways consonant with society as a whole!) We’re not using it to augment us in ways that are beneficial, instead we’re using it in ways that produce short-term savings at the expense of long-term benefits. And that, to me, is a path to a worse future, and I think we can do better.

Overall, the rush to ‘AI’ is pretty short-sighted. Like most things, we see an overexcitement, and then we face the reckoning. The problem is that this rush has been substantially bigger than any ever before, and the consequences of the resulting rationalization could be onerous. Maybe it’s time, as we’ve seen before, that we slow down and look at how to use this new tech appropriately. I like a meme I just heard: instead of move fast and break things, move slow and fix things. Is it time?

Knowing Cognition

11 August 2026 by Clark Leave a Comment

Regardless of what tech stack you’re using, it’s increasingly clear that tech is changing the environment in which we work. In particular our cognitive environment. The generative artificial intelligence wave has made clear that the ways we use technology are likely to change. There are lots of issues, so how do we handle it? As a complement to my post about technologies, I think that a necessary component of an AI approach will increasingly be an understanding of human learning, that is: knowing cognition.

Using ‘smart’ technologies is changing how we think, work, and learn. Ed Hutchins helped us understand distributed cognition, but we’re also seeing that it has effects if used wrongly. For instance, folks seem to be less connected because the technology substitutes. There are also impacts on what we learn when we use technology as a substitute for thinking, apparently an all-too-common phenomena. That means we need to know when, and how to use technology to supplement our thinking, not replace it.

And, yes, we definitely change how we do tasks when we can. With the advent of the GPS, we changed how we navigate. We no longer needed to bring, and be able to interpret, a mapbook. Though it helps to still be able to use one. Particularly when the GPS sends you awry. Similarly with digital address books; I don’t know my kids phone numbers, because they’re in my system!

Moreover, we prefer to outsource different things. Some folks still want to code, while I’ve left that. Others have handed off some tech responsibilities; my wife now depends on me to use the television! Which is all fine, if we’re making conscious decisions. When we become dependent on someone or something without recourse, however, we can face problems.

Which brings us to the question of how we make the decisions. I’ll suggest that if we don’t know what matters in our personal choices of cognition, we won’t make good decisions. That goes, by the way, for whoever is making decisions (we hear of execs who have unrealistic expectations of what the technology will do). I’ve long argued that what will be the sustainable knowledge base, going forward, is what technology can do (and what it can’t), and how people think. And work. And learn. Plus, some design and business basics, probably. That’s relevant here.

To make good decisions about what you can outsource, or not, depends on what its impact will be on your thinking. Does it support you making good decisions, and improving them? Or are you outsourcing them, and therefore diminishing your ability to make them? It may be as simple as that, but I believe it’s more. I’m probably biased, but even with that caveat, I’m inclined to think that everyone needs to know the basics of cognition. (Hence why I made my take freely available.)

I continue to think that we have to do the thinking first. It doesn’t have to be elegant, a sketch or a few bullets will do, but you have to do the important thinking first. Then, you can use tech to also address that thought, and use the tech as a way to get feedback. However, you’re the one analyzing results, seeing what it comes up with, improving your thoughts if it’s come up with something you like, and rejecting anything that doesn’t resonate. It’s a support, not a replacement.

I wanted my Learning Science book to not say ‘“or Instructional Designers” in the title, though the publisher realized that’s their market, because we’re all instructional designers. (And, to be fair, the later chapters err on the side of learning, as opposed to interface design or public speaking. Still…) Whether for ourselves, our kids, friends, there are always people we’re helping learn. We may not have the formal role, but we do a better job when we know how we think.

So, in addition to suggesting a focus on small hybrid multi-agent systems for AI, I also think we need to also personally know cognition, and learning in particular, so we know how and when to augment our thinking. And, when not to. That’s why I’m involved with the Learning Development Accelerator, by the way. And am working on a ‘learning to learn’ curriculum. Those are my thoughts, what are yours?

Small hybrid multi-agent systems

4 August 2026 by Clark Leave a Comment

So, I’ve been pretty harshly critical of the current generative artificial intelligence (Gen AI) situation. Again, I have no problem with the tech itself, but the big providers I have several problems with. Not to rehash those objections, but I’ve been investigating alternatives. And, I think I’ve now converged on a solution: small hybrid multi-agent systems. I owe thanks to several informants, including Elizabeth Dalton, Markus Bernhardt, and Scott Parker.

First, despite my other objections, what I see is that the companies are pushing these changes as ‘inevitable’ as well as desirable. And, sadly, way too many orgs have jumped on board. Which, while understandable, isn’t desirable. What we’re now seeing are the results. Companies are hiring folks back that were fired. We’re also seeing that we’ve hollowed out the pathways for new hires to acquire the necessary skills. Which is part of the larger picture of negatively impacting learning.

Yet, I’ve been a proponent of AI in the past. The technologies on tap have real benefits, whether they’re AI or something that we now understand so we can’t call it AI anymore. So I need to converge on an integration. And, that utility for specific needs is the key to my reconciliation.

In interpersonal situations, I remain a strong belief in ‘partnering’. That is, recognizing that others have complementary knowledge to your own, and different perspectives. So, why have one supposedly all-seeing all-knowing solution, instead of smaller more targeted ones?

Ok, yes, there’re issues of cost. Right now, it’s seemingly cheap to get an AI to do your rote tasks. However, two things are relevant here. For one, the costs are going up. Companies are going public, and that means that they’ll have to start generating revenues to repay the venture capital investments. And we’re seeing that companies are realizing that their AI expenses are growing, to the extent in some cases where they’re more than the people they’re replacing. We’re also seeing that they haven’t yet faced all the incumbent costs: IP, environment, labor. That’ll change the game too.

The flip side is that smaller language models are more cost-effective. There are open source models that you can obtain, install locally (or on private servers), train on specific documents, and use for specific purposes. Yes, there’s the development costs, but they’re more secure, faster, and less costly. As well as addressing the other concerns.

Moreover, you can have more than one. You can have specialized agents to handle X, Y, and Z. If it’s a language task, you can use a language model. If it’s another type of task, you can use another model. You can even mix in symbolic with sub symbolic, and programs versus AIs. If diversity is good for people, it also appears to be useful for systems. A mix of custom agents with an agreed upon interchange protocol is, it appears, more cost-effective, private, maintainable, and more.

Look, if you don’t mind the costs, and the questionable ownership of data, and security, and dumbing down your workforce, and … go with the big ones. They certainly will benefit. But if you want to be secure and sustainable, small hybrid multi-agent systems makes more sense to me, at least in many instances. Sure, language models trained on all the language out there is useful. Er, for general purpose language tasks. So make that one agent in your solution, if necessary (tho’ aren’t your language tasks typically more constrained?). Which, I’ll suggest, are a small subset of all the things you need to get done by technology. As I learned from the Aussies (originally a Brit saying), “horses for courses”, in this case meaning use the right tool for the job. Am I missing something?

The right questions?

21 July 2026 by Clark Leave a Comment

Like others, I’ve been somewhat less than thrilled with the decrease in quality we’re seeing in more and more of our experiences. Such that Cory Doctorow even termed it “enshittification“. Of course, I haven’t read the full expose, but I also see it in what L&D is doing. Which leads me to wonder if we’re asking the right questions, or focusing appropriately on same.

It’s no secret that organizations are focusing on short-term shareholder returns. And I get it, but…I wonder if that’s the best focus? An apt focus would be on generating both revenue and brand loyalty, which should lead to the returns. However, it seems that the focus is different. And the introduction of generative AI seems to exacerbate that, in that it’s use is too often to produce more, cheaper. Oddly, there don’t seem to be similar rewards for doing more with less…

The appearance is that the focus is on driving more output faster and cheaper. That is, indeed, one way to shareholder returns. However, it seems to me, it also undermines brand loyalty, and thus the long term outcomes are worse. Are we too focused on the ‘short term’ aspect? Sure, shareholders want returns, but they can flip to other investments, so maybe it appears good to deliver? If we just focus on extracting more money, it would seem that it would undermine trust.

Another question we could ask, instead of ‘faster and cheaper’ would be better. How can we increase the customer outcomes and satisfaction? How can we make a better experience? When I look at L&D, for instance, the people I see offering better solutions are doing that. Whether it’s Jess Almlie focusing on being a better business partner, or JD Dillon helping the frontline more effectively, Lori Niles-Hoffman with tech, etc, the answer always is to do better. Which I laud.

To be fair, I’m not a stock investor. I don’t play that form of gambling. Yes, I have investments (all too few, TBH), and they’re managed. I indicate my preferences, and they execute them. However, I’m focusing on long-term benefits. I wish more companies would do that too, particularly online. And I wish more folks would be doing that in the areas they control, say L&D ;).

KMN

16 June 2026 by Clark 6 Comments

So, I’m compiling a hopefully comprehensive list of learning to learn strategies. As part of that, I searched on meta-learning strategies. And, in two of the top listed sites, I found, indeed, articles on meta-cognitive strategies. And, a myth. More specifically, one that continues to bedevil our industry (and, relatedly, education). I mean, we’re a quarter way into the new century! I sadly feel like KMN (Kill Me Now).

So, the first site had an article that asked “What meta-learning techniques improve skill acquisition?” After first saying why and what, both good things, the very first recommendation is “understand and adapt to your learning style”. What? I mean, this is such a debunked theme that it’s almost ludicrous. I did comment, but yikes.

So, then going through my other tabs opened from the search results (a meta-learning strategy, btw ;), I find another list. This site has an article listing Examples of Metacognitive Strategies. And, it was only item 5 that was “awareness of learning styles”. Still, here’s a myth pretending to be a valid strategy. Here I couldn’t comment, but I did send a politely worded recommendation to pay attention to the research. (And got a response saying they’d moved on from that position. Ok, but then why did I find it when I did the search?)

In both cases, pointing to an approach that has been deeply investigated and resoundingly dismissed undermines anything and everything else they have to say. Why should I trust what you tell me when I know part of it is wrong? There was other good, and bad, advice in the articles, but sorry, you’ve lost my ability to think you know what you’re talking about.

Yes, I get that the idea of learning styles feels right. Anyone who’s taught recognizes that people learn differently. But…what people think is good, and what actually works, has essentially zero correlation. Yes, you should have different representations. This is to increase access, not to address learning styles, however. And deliberately designing for styles is clearly a waste of money. For instance, creating three different versions of the content for say, visual, auditory, and kinesthetic learners, wouldn’t be justified.

A step back provides this lesson: know what you say before you say it, and don’t say wrong things unless you want to lose credibility. Which is kind of a meta-comment, but that’s a separate issue ;). I don’t really want you to KMN, but…sometimes it feels like we are moving backwards. Can we return to using what’s demonstrably known as a basis? Sure, science gets upgrades, and so sometimes is wrong (flat earth, anyone?) when it moves to a better explanation, but it’s still better than a basis of dogma.

Partnering

26 May 2026 by Clark Leave a Comment

Too often, I’m prone to think about just doing things. For instance, I advocate for L&D to be responsible for performance support, innovation, etc. Yet, there may be groups already doing this, whether ad hoc or organizationally mandated. And…one thing is for sure, you don’t want to reinvent the proverbial wheel. If someone’s doing something, work with them, not on your own. That is, partnering. And, of course, the term is well-known (e.g. listening to Dawn Snyder from our LDE conference). But, what does it mean?

It turns out, in thinking through doing things like performance support well, it’s not just the design of the resources, but it’s also their availability. Many years ago, i talked about the elements needed for content, what Brent Schlenker talked about: the 5-ables. What matters here is that someone may already be responsible for this! It might be in the web team, or the knowledge management team. Sure, if no one’s doing it, by all means take it on. But if someone is, don’t tread on their turf.

Of course, that doesn’t mean you can’t offer to help. If you know more about design of such resources (e.g. visual design), offer that assistance. If you’re concerned about curriculum and coverage, and they’re not, that’s another area to contribute in. Maybe they don’t have good governance, creating things and letting them languish un-updated and maybe expired. It could be that they’re not using technology well, or making it available by role instead of silo. There’re lots of aspects to get this right, and if you can supply any missing bits, all to the good.

The same goes for community and innovation. There could be folks working on either or both. Are they supporting informal learning? Is the culture aligned? Do they promote good practices? All these are areas that could be a contribution from L&D. Not that you have to own it; if you can add value to create a better overall solution, that’s great!

All told, it’s about seeing what skills are already extant, and what’s missing. And offering that in a way that’s not threatening, but seen as adding value. You don’t want to take over things that people want to own, but you do want to make sure they’re doing it well, and in conjunction with an overall org-wide learning strategy. Becoming a learning organization is no longer a nice-to have; the ability to be agile, to adapt, is going to increasingly be the only sustainable differentiator. As a consequence, orgs need to ensure that they’re aligning the elements. You don’t have to drive it (unless it doesn’t exist), but you want to align and improve.

That’s a valuable contribution, regardless of locus. Improving ideas and people has to be in the interest of the org, and of course of L&D. Further, assisting is less-resource intensive than owning. It helps things work better and reduces the needs on your resources, so you can devote efforts elsewhere. Even work to develop the ability so you can release it. I reckon it’s ‘partner where you can, drive where you must’, but make sure the org’s getting better over time. Ultimately, that’ll be because you care, and that’s what you care about. Right?

The right tech for the job?

19 May 2026 by Clark Leave a Comment

Ok, so this blog is for my musings, and this is very much a musing. However, a couple recent things have prompted some thoughts. The issue is Large Language Models (LLMs). As I’ve said, I have no problem with the tech inherently. It’s really optimized for language roles (as the name implies). What is concerning to me is the hype, and so the use. And, it’s led me to wonder if it is, or what is, the right tech for the job.

So, up until LLMs, when you wanted something done, you built the appropriate tech. You put together specifications, and development teams built it. (And then they asked UI to fix the problems, as Don Norman talked about in The Invisible Computer. Probably then handed off to training folks to address the problems from the bad design outside the UI.). It took time, and money. And, if you didn’t use something like Watts Humphrey’s Personal & Team Software Processes, you likely took too long, and had too many errors.

You could use AI for more decision tasks. So, either symbolic if well-defined, or based upon training data and machine learning if ill-defined in principle. You, of course, still have to live with or address the biases in the rules or databases. And, of course, the brittleness at the edges of the decision space. Still, all told, we had approaches. Then, the world changed.

So, for one, my colleague Kevin Wheeler (a deep expert in the talent world), talked about how the tasks of the talent function are in flux. What he cited was that many of the rote tasks were being made redundant. Which is good, I opined, if we’re removing tasks that people aren’t good at (I’ve said before that we should be doing pattern-matching and meaning-making, and leaving rote to the machines). However, there’s the problem of developing the expertise. So, for instance, as Etienne Wenger and Jean Lave talk about in communities of practice, moving from peripheral tasks to central as you understand the domain, But you need those peripheral tasks!

Plus, we’re seeing people being laid off. Meta just announced another 8000 being laid off, for efficiencies, and there have been announcements from many of the big orgs (and small ones are making similar decisions). Cutting through the smoke, what we sees is that folks face increasing expectations to use AI to do things faster, and the expectations are increasing (without, mind you, an increase in rewards for same). But, increasingly we’re not working together as we used to.

What also showed up in a LinkedIn conversation is the expectation that we can ask LLMs to do the things that we used to do by writing software. And yes, such systems make mistakes, but so do humans, right? Yes, and…we can assign responsibilities with humans, and they’ll be corruptible and more, but we have compliance to deal with that. As Markus Bernhardt is pointing out, we’re not doing a good job on that with our systems. We, too often, haven’t worried about the necessary guardrails, and security, and responsibility, and governance, and….

What is concerning me, as folks like Mark Britz talk about, is that we’re losing the human connection. We’re losing:

the upward path for new folks
the continual development of our own capability
the necessary checks and balances that keep our systems secure

Really, we’re turning away from doing things together to maximize outcomes, and instead are working to be more expedient. In short, we’re trading off effectiveness to achieve efficiency. And ignoring ethics along the way.

In short, I fear we’re using the ease of doing things with LLMs to avoid the hard work of doing things the right way. Which I resist. I am seeing quite the backlash amongst the folks forced to use AI. Which includes the pressures by execs to use it, and the eagerness of those to promote it who stand to gain (whether vendors or consultants). We see research results showing that folks are thinking less, execs have unrealistic beliefs about how productive it makes people, and it’s pretty simple to recognize that the costs can’t stay as they are. The illusions from smoke and mirrors aren’t a good basis on which to plan.

Don’t get me wrong, I do see the benefits of LLMs. I just see them in context of the bigger picture of people, tech, and the broader picture of AI. And I could be wrong, it’s true. It’s just that there’re some reasons to believe that decades of immersion in the relevant fields have some basis for questioning whether we’re using the right tech for the job. I welcome your thoughts!

Another Book?

30 April 2026 by Clark Leave a Comment

So, I did a thing. Yes, I wrote yet another book! Well, it’s shorter than my (already short; I’m either terse or lazy ;) existing books. So, it’s an ebook only. But why? Well, that’s worth a small story.

When we (Learning Development Accelerator; LDA) run our Learning Science conference, we regularly get plaudits and requests for more detail on putting learning into practice. It’s also the reason we run the Learning & Development as Ecosystem conference (going now, next week are the live sessions; not too late. but time’s running out). Which covers everything else besides learning design.

But what I didn’t feel existed properly was a book addressing the pragmatics of doing L&D. There are strategic books, and process books, but not just the nuts and bolts. So I wrote it.

So that’s why I have put out the ebook Designing for Learning in the Real World. It’s my personal take on the things I’ve seen, from my perspective as a sort of ‘outsider’ to L&D. That is, it’s about how to do all the things L&D should be doing. It’s learning, of course, but also the performance ecosystem – job aids, resources, community – and all the other things: tech infrastructure, culture, etc.

It’s short, it’s focused, and it’s now available ;). There are lots of ways to find out my thinking, e.g. this blog, previous books, etc. This is just a very condensed look at everything I have seen about how to do a good job. My thoughts, encapsulated. We now return you to your regularly scheduled blog, already in progress…

Ethics, science process, and disagreements

14 April 2026 by Clark Leave a Comment

Recently, one of our LDA members provided a link to this argument, as a follow-on to a book club discussion. The book we covered in this meeting was David McRaney’s How Minds Change (a worthwhile read), and the article raises an issue that links science process, discussions, and society. Hence, it’s extremely interesting. This argument covers science process in a particular instance of theorizing, so it’s worth thinking about.

To start, the article talks about two theories of foundational disagreement. He cites the prominent theory of Jonathan Haidt’s about five (or six, or?) foundational moral values. I was sold on that theory after reading Haidt’s The Righteous Mind (as recommended by my late mother!). So, to see an alternative was of interest. And, I have to say, that the evidence makes sense. The article does a good job of laying out the competing approaches, which is interesting in and of itself. There are questions of methodology, allowing for some divergence. In short, why do we agree and disagree?

Of equal, if not more interest, however, is the discussion of the process of the debate. That is, as Naomi Oreskses points out in Why Trust Science, science is about consensus. And Haidt’s been a good popularizer of his model. When a challenger to the entrenched theory arises, we end up with a battle of ideas, ala Thomas Kuhn’s The Structure of Scientific Revolutions. The better explanation takes a while to establish the data, and the acolytes of the previous approach mount a defensive action, but ultimately we accept the new story. If, and this is important, it continues to demonstrate superior explanatory power.

The article lays out, in interesting prose, the nature of the arguments, but also the nature of the process of argumentation. There’s presentation, and attacks, and in short a riveting story of science in action. Yet, it’s happening (largely) as science, with the arguments based on data, not on personal attacks.

It also raises issues of the overall ethics of the science process. This is relatively personal, as it’s a debate I’m having with a family member. In short, is the scientific process to be trusted, or is it too biased and we need an alternative? This gets into Science Denial, as Gale Sinatra & Barbara Hofer’s book discusses, That’s what I’m faced with, sadly.

As with some other things, I suggest that while science isn’t perfect, it’s better than any other approach. Sure, there continue to be misuses, as have the products that science provides us. I’ve got bias, as I’ve had science training and have been an active participant as well as translator. So take what I say with the proverbial boulder of salt. However, if you want to quibble, you may have to travel by horse to talk to me in person; you’re reading this via the benefits of the science process, and you wouldn’t want to be hypocritical, right?