What does it mean to be a master of a craft?
Alasdair MacIntyre’s notion of the goods internal to a practice self-consciously separates the standards of practitioners from mere material gain, which constitute “external goods”. As a follower of McCloskey, it seems clear to me that the sacred and the profane are not so cleanly separable. Previously, when I have written about this, I argued that external goods must be internalized in some way. That is, part of the way you become a good carpenter, or good lawyer, or good salesman, is by making money in a good way. That is to say, part of the ethics of a practice is precisely how you deal with your customers or clients or patrons or donors.
If you buy the argument that internal goods exist, what you think about the nature of them probably relates to your beliefs about the nature of morality in general. I have my own thoughts on the matter.
What MacIntyre’s line of thinking attempts to do is exclude the customer from the bargaining table. But that is not right. Whatever you think about the aesthetic and ethical commitments of the carpenter, surely a chair needs to be something that someone could actually sit on. And surely a doctor is supposed to help his patients return to health when they have become sick. Obviously the carpenter knows more about carpentry than his customers, and the doctor knows more about medicine than his patients. But someone who buys a chair for his own use has to live with the experience of sitting on the chair, or consider throwing it out and judging the expense a waste. And someone who sees a doctor has to live with the doctor’s mistakes, as the loved ones of mothers who developed puerperal fever and died did.
Customers and clients and patients deserve a voice in the conversation about goods internal to practices. In MacIntyre’s scheme, money can only ever be an external good. But it seems to me that in a healthy institution, money becomes one avenue of legitimate feedback into the judgment of the specialists attempting to arrive at a shared understanding of the good. Adam Smith clearly thought so, as he apparently believed that offering a coin was an act of persuasion.
Make Your Standards Your Own
When they taught us sonnets in grade school English, they emphasized that while geniuses like Shakespeare frequently broke the rules, you have to learn them first before you can understand how to break them.
At the time this seemed fairly inscrutable, though I took their word for it. Now, I think I understand—which is itself an example of the same process. It’s about making a skill your own; about truly becoming a part of the community of practitioners.
Let’s consider that famous Chesteron passage again:
In the matter of reforming things, as distinct from deforming them, there is one plain and simple principle; a principle which will probably be called a paradox. There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, “I don’t see the use of this; let us clear it away.” To which the more intelligent type of reformer will do well to answer: “If you don’t see the use of it, I certainly won’t let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.
Now compare it with Edmund Burke’s even more famous passage:
We are afraid to put men to live and trade each on his own private stock of reason; because we suspect that this stock in each man is small, and that the individuals would do better to avail themselves of the general bank and capital of nations and of ages.
Both are invoked by traditionalists, but the character of the claims is quite different. The “general bank and capital of nations and of ages” is rather a black box. Meanwhile, Chesterton makes his fence much more contestable, open to negotiation if you can figure out what it is doing there.
As I said, each practice has its Chesteron’s fences. Most practitioners attempt to carry the practice forward by refining it within the boundaries of those fences, perhaps pushing them out a bit here and there. Sometimes they’re replaced entirely with something else—after all, there must have been a moment when the fences were built in the first place, right?
Like Shakespeare, most people who master a practice operate outside the fences quite regularly. What makes them different?
Here Julia Annas’ take on skill development from Intelligent Virtue comes to mind:
What the learner needs to do is not only to learn from the teacher or role model how to understand what she has to do and the way to do it, but to become able to acquire for herself the skill that the teacher has, rather than acquiring it as a matter of routine, something which results in becoming a clone-like impersonator.
The standards of a practice are, first and foremost, tools of educating new practitioners, who need greater and stricter guidance than experienced practitioners, never mind masters. Gaining flexibility is a matter of gaining confidence, of making the reasons behind these standards your own.
But even masters rely on them to an enormous degree.
Some people get confused and think that these standards are themselves the goods internal to the practice. In reality, these standards constitute a consensus among practitioners—often established much earlier—as to the best ways to consistently get as good a result as possible with the current state of the practice.
Consider the case of surveys. Researchers want to select people at random to fill out the surveys, because statisticians have worked out that picking enough people at random from the general population will shrink your sampling error. That is, randomness and a sufficiently large number of respondents will allow you to generalize the results of your survey beyond your sample.
But how does one select people at random, in practice? The answer that polling companies ultimately came up with was random digit dialing; machines generate phone numbers at random and dial them. At the dawn of the era of surveys, the diffusion of landline phones had hit a critical mass, making this approach quite viable. Nevertheless, there were always biases. For most of the high years of random digit dialing, you were much more likely to get a housewife on the phone than their proportion to the population.
For this and other reasons, polling companies have engaged in weighting their samples to make them more representative. But this trades off some of your randomness for the end of having a more visibly representative sample. Yet no one would say that you could pick your sample non-randomly from the start, so long as it is perfectly, demographically representative of the population at large. So we’re entering dicey terrain here, statistically speaking. Still, polls do quite well at predicting things like election results. So random digit dialing plus a bit of tinkering after the fact seems to have been effective, for the most part.
Nonresponse has drastically increased in recent years with the switch from landline phones to cell phones. As a result, the odds that the respondents are sufficiently different from the general population to introduce a serious sample error have gone up as well. Researchers have to lean more and more heavily on weighting and other adjustments, meaning that the problems that those introduce are growing in their impact.
As a result, seasoned veterans are experimenting with alternatives. On an old EconTalk, Doug Rivers described the approach taken by his employer, YouGov. From a database of the general population, they randomly select specific people. They then give the survey to people who have opted into the system, to take surveys in exchange for money. They only give it to people who are demographically identical to the ones chosen randomly from their database. Essentially, they are randomly selecting the demographics, rather than the people, and banking on that being better than random digit dialing plus weighting.
This is called sample matching, and the results are mixed. For the most part, it seems equivalent to the traditional method in the one area we can really measure result quality the most easily, election surveys. Some versions of sample matching are drastically worse than traditional random digit dialing. But it keeps getting refined, and perhaps it will one day become the standard among political polling. It is already quite common in market research.
What I want to emphasize is that sample matching is an idea that has been around for decades. It has only more recently been seriously attempted. For most of the time it was under discussion, I can imagine some ambitious young researchers feeling frustrated by the tyranny of the random digit dialing standard. After all, it has all of these known problems! But most of the time, stepping outside of the known problems means simply facing immense uncertainty. The odds you will stumble upon something better, which the more experienced members of your field simply failed to find before you came along, seems small on its face.
When you have really mastered the standard approach, when you really get where weighting and other tinkering work and where they break down, as much as anyone can grasp such things…that is when you can stand before Chesterton and negotiate.
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