Rasmussen on Chiasmus in the Book of Mormon

[Billy Shears]

Hi Billy! I've missed you! I was worried that COVID might've taken you away like a thief in the night, which would've grieved me deeply.

I’m fine, thanks. I’ve just been busy in real life, and have lost most of my interest in your analysis.

I make note of this, but I also don't make much of it. Maybe it's an indication that repetition is just a part of Joseph's (fake) revelatory process, and maybe it's an indication that the D&C wasn't originally composed in English. But I'm not sure it's an indication of either. The D&C is a different beast than the Book of Mormon entirely. As has been pretty easy to notice this year, the D&C employs quite a bit of apparently stock language when it comes to forming various blessings and mission calls. That sort of thing could quickly decrease working vocabulary, but it's also not something we really see in the Book of Mormon. There you have repetition in the form of chiasmus and parallelism and (often subtle) internal allusions, but you don't really get stock bits of language copied and pasted every other chapter.

Stuff like the D&C is why I think we need a deeper dive into the TTR and what drives it, both in the Book of Mormon and in comparative contexts. My essay may or may not stimulate that kind of effort, but a guy can hope. In the meantime, it serves as a decent barometer for what I see as the probability of Joseph or anyone else in the 19th century giving us the chiastic and parallelistic structures we see in the Book of Mormon.

This is the type of comment that makes it so hard to take you seriously. In the real world, if somebody had a hypothesis that TTR (or anything else) is a reliable indication of the existence of uniquely-ancient literature, I’d expect him to test his assumption. If somebody finds multiple examples of modern books with high inverse-TTL’s ratios (e.g. The Doctrine & Covenants and Green Eggs and Ham), that proves the assumption isn’t true. A responsible researcher would reject their hypothesis, or at a minimum qualify the results in a quantitative way.

You might intuitively think “the probability of Joseph or anyone else in the 19th century giving us the chiastic and parallelistic structures we see in the Book of Mormon” is a hundred-trillion to one, but if that’s what you think just state it—don’t try to impress your reader with a whole bunch of math based on assumptions that you’ve proven are false.

[Doctor CamNC4Me]

Which is more likely?

1) Word repetition correlates to oral dictation of stories and sermons by someone with a limited, Biblically inspired vocabulary.

2) Word repetition correlates to ancientness, due to a high density of Hebraic chiasmus.

As a layperson without any statistical training I’d go with #2.

-_-

You don’t say.

[Billy Shears]

For anyone not up to speed with what these terms mean, a probability distribution function is a general term for any function that models how likely a set of events are. The cumulative distribution function, on the other hand, is a function adds up a set of probability values for a range of various outcomes. For instance, you could ask what the probability is that someone could have a child that's 6'7". In doing so, you could examine the probability distribution function for height, and then look at the number of children born where were exactly 6'7". That number would probably be pretty low. But it's also probably not the question you really care about. In that case, you really care about the more general question: what's the probability that someone could have a child 6'7" or higher--in other words, instead of asking what the probability is of getting the exact result (6'7"), you're asking what the probability is that you could get a result similar to that (6'7" or above). When asking statistical questions in the social sciences and elsewhere, we generally are asking the latter kinds of questions, and the same goes for my explorations of Book of Mormon evidence. I'm usually asking what are the chances we'd get evidence as strong or stronger than what we see, assuming the book was fraudulent as well as assuming the book was authentic. Chi-squares and t-tests and ANOVAs are pretty good at producing those sorts of probability estimates.

Yes, this is how traditional Fisherian statistics works. But that is not how Bayesian statistics works. Choose one or the other. Don’t combine the two into something that is mathematically invalid.

If I'm following Billy here, it seems that he would prefer that I instead produce estimates for the probability of producing the specific results that we see and only those specific results, and that it's somehow inappropriate that I make use of stats that rely on cumulative functions. That would be pretty dumb. In fact, it would probably lead me to engage in the kind of sharpshooter fallacy that he's been accusing me of since the start of this essay series.

No, this has absolutely nothing to do with what I would prefer. What it has to do with is Bayes’ Theorem. Bayes’ Theorem is an actual mathematical theorem—it isn’t an arbitrary formula that people can change to fit their fancy.

In short, Billy's critique here is incorrect (and a bit pedantic).

That is objectively false. We can come to different subjective impressions of how likely it would be for a 19th-century author to write a chiasmus. In contrast, Bayes’ theorem is a specific mathematical theorem. The theorem does NOT say what you think it says.

What’s clear is that you have a lot of practice with Fisherian statistics—you think in terms of p-values and could do a chi-squared test in your sleep. That’s great!

What is also clear is that you've only studied Bayesian Statistics in the context of rudimentary discrete distributions. You then took off on this ambitious project and made some extrapolations about how to do Bayesian Statistics with continuous distributions, based on your background with Fisherian Statistics.

Your guesses on how to approach Bayesian analysis with continuous probability distributions were wrong.

Objectively wrong.

Mathematically invalid.

The consequent probability is NOT a p-value!!!! A mathematically valid consequent probability comes from a likelihood function. It does not come from a Fisherian p-value. They are two different and incompatible things. If you redefine the Bayesian consequent probability to be a Fisherian p-value, you disprove Bayes’ Theorem.

[Doctor CamNC4Me]

All I know is if The Late War contains chiastic structures Imma put the odds of the Book of Mormon being “true” at just greater than 10 to the 80th power.

But that’s just a hunch.

Am I a PhD now?

- Doc

[Lem]

Speaking of bad assumptions, I'm not a linguist, but even the most rudimentary efforts to look at the literature seem to indicate how bad Rasmussen's assumption is that inverted TTR, or word repetition measure, correlates with chiasmus. Here's one sample:

3 A New Approach to Chiasmus Detection

To make progress, we need to move beyond the binary definition of chiasmus as simply a pair of inverted words, which is oversimplistic. The repetition of words is an extremely common phenomenon. Defining a figure of speech by just the position of word repetitions is not enough (Gawryjolek, 2009; Dubremetz, 2013). To become a real rhetorical device, the repetition of words must be “a use of language that creates a literary effect”.1 This element of the definition requires us to distinguish between the false positives, or accidental inversions of words, and the (true) chiasmi, that is, when the inversion of words explicitly provokes a figure of speech.

Rhetorical Figure Detection: the Case of Chiasmus, 2015

https://www.researchgate.net/publicatio ... f_Chiasmus

Note that this says that even the position of repetitive words is insufficient evidence for Chiasmus. Rasmussen doesn't even go that far, as he assumes that simply the ratio of repeated words to the total, distributed anywhere, correlates with chiasmus.

[Dr Moore]

As I understand it, the Mopologist's claim is that "Joseph couldn't have known about chiasmus."

Actually, if you read my essay closely, my argument's a bit different than how you've framed it. Though I suggest that it's unlikely that Joseph would've known much about chiasmus, I argue that even if he did know something about it, both the frequency and distribution of the chiasmus we see in the Book of Mormon remains unexpected, particularly in comparison to other psuedo-biblical works.

Cheers!

I was referring to the Mopologetic arguments, not your essay.

The second part of your comment about "frequency and distribution" remaining "unexpected" is non sequitur as to the question of ancientness or historicity.

[MG 2.0]

As a layperson without any statistical training I’d go with #2.

-_-

You don’t say.

I do say.

Regards,
MG

[Lem]

For anyone not up to speed with what these terms mean, a probability distribution function is a general term for any function that models how likely a set of events are. The cumulative distribution function, on the other hand, is a function adds up a set of probability values for a range of various outcomes. For instance, you could ask what the probability is that someone could have a child that's 6'7". In doing so, you could examine the probability distribution function for height, and then look at the number of children born where were exactly 6'7". That number would probably be pretty low. But it's also probably not the question you really care about. In that case, you really care about the more general question: what's the probability that someone could have a child 6'7" or higher--in other words, instead of asking what the probability is of getting the exact result (6'7"), you're asking what the probability is that you could get a result similar to that (6'7" or above). When asking statistical questions in the social sciences and elsewhere, we generally are asking the latter kinds of questions, and the same goes for my explorations of Book of Mormon evidence. I'm usually asking what are the chances we'd get evidence as strong or stronger than what we see, assuming the book was fraudulent as well as assuming the book was authentic. Chi-squares and t-tests and ANOVAs are pretty good at producing those sorts of probability estimates.

Yes, this is how traditional Fisherian statistics works. But that is not how Bayesian statistics works. Choose one or the other. Don’t combine the two into something that is mathematically invalid.

If I'm following Billy here, it seems that he would prefer that I instead produce estimates for the probability of producing the specific results that we see and only those specific results, and that it's somehow inappropriate that I make use of stats that rely on cumulative functions. That would be pretty dumb. In fact, it would probably lead me to engage in the kind of sharpshooter fallacy that he's been accusing me of since the start of this essay series.

No, this has absolutely nothing to do with what I would prefer. What it has to do with is Bayes’ Theorem. Bayes’ Theorem is an actual mathematical theorem—it isn’t an arbitrary formula that people can change to fit their fancy.

In short, Billy's critique here is incorrect (and a bit pedantic).

That is objectively false. We can come to different subjective impressions of how likely it would be for a 19th-century author to write a chiasmus. In contrast, Bayes’ theorem is a specific mathematical theorem. The theorem does NOT say what you think it says.

What’s clear is that you have a lot of practice with Fisherian statistics—you think in terms of p-values and could do a chi-squared test in your sleep. That’s great!

What is also clear is that you've only studied Bayesian Statistics in the context of rudimentary discrete distributions. You then took off on this ambitious project and made some extrapolations about how to do Bayesian Statistics with continuous distributions, based on your background with Fisherian Statistics.

Your guesses on how to approach Bayesian analysis with continuous probability distributions were wrong.

Objectively wrong.

Mathematically invalid.

The consequent probability is NOT a p-value!!!! A mathematically valid consequent probability comes from a likelihood function. It does not come from a Fisherian p-value. They are two different and incompatible things. If you redefine the Bayesian consequent probability to be a Fisherian p-value, you disprove Bayes’ Theorem.

Thanks for taking the time to spell this out Billy. I get the sense Rasmussen is counting on people not actually reading what he writes. He has some real whoppers in there, doesn't he?

[Moksha]

Why would the Nephites have adopted a continuing convention of Biblical chiasmus when they didn't even have their own language and had to use Reformed Egyptian on their gold plates?

[Doctor CamNC4Me]

Why would the Nephites have adopted a continuing convention of Biblical chiasmus when they didn't even have their own language and had to use Reformed Egyptian on their gold plates?

Uh. Hello! They had their own language, Moksha. Pfft.



- Doc