The adjective dialectical describes ideas reasoned about through dialectic, or the interaction of opposing or contradictory ideas. However, it is often used to in a rather different sense: ‘pertaining to dialects’. For that sense, the more natural word—and here I am being moderately prescriptivist, or at least distinctivist—is dialectal. Dialectical used for this latter sense is, in my opinion, a solecism. This essentially preserves a nice distinction, like the ones between classic and classical and between economic and economical. And certainly there are linguists who have good reason to write about both dialects and dialectics, perhaps even in the same study.
Category: Presentation of self in everyday life
On “significance levels”
R (I think it was R) introduced a practice in which multiple asterisk characters are used to indicate different significance levels for tests. [Correction: Bill Idsardi points out some prior art that probably predates the R convention. I have no idea what S or S-Plus did, nor what R was like before 2006 or so. But certainly R has helped popularize it.] For instance, in R statistical summaries, * denotes a p-value such that .01 < p < .05, ** denotes a p-value such that .001 < p < .01, and *** denotes a p-value < .001. This type of reporting increasingly can be found in papers also, but there are good reasons not to copy R’s bad behavior.
In null hypothesis testing, the mere size of the p-value itself has no meaning. All that matters is whether p is greater than or less than the α-level. Depending on space, we may report the exact value of p for a test (often rounded to two digits and “< .01″ used for abbreviatory purposes, since you don’t want to round down here), but we need not. And it simply does not matter at all how small p is when it’s less than the α-level. There is no notion of “more significant” or “less significant”.
R also uses the period character ‘.’ is used to indicate a p-value between .05 and .1. Of course, I have never read a single study using an α-level greater than .05 (I suppose this would simply make the possibility of Type I error too high), so I’m not sure what the point is.
My suggestion here is simple. If you want, use ‘*’ to indicate a significant (p < α) result, and then in the caption write something like “*: p < .05″ (assuming that your α-level is .05). Do not use additional asterisks.
Avoid adjacent delimiters
A mundane but highly effective writing tip is to avoid structures like “…) ( …” in your writing. For instance instead of
As argued by Chomsky & Halle (1968) (henceforth, SPE)…
you can (and should!) write
As argued by Chomsky & Halle (1968; henceforth, SPE)…
which I think you’ll agree Just Looks Better. A closely related trick is to avoid things like
The Greek letter Υ denotes /y/, /yː/…
and instead write
The Greek letter Υ denotes /y, yː/…
You can do this with phonemic forward slashes, phonetic square brackets, or the curly braces used to specify sets.
Quiet quitting is work-to-rule but worse
This week’s hot media trend is quiet quitting, and if you’re even remotely familiar with the US labor movement, you’ll recognize this as a version of organized labor’s work to rule actions, in which workers do the absolute minimum amount of work required by the contract. The difference is that a quiet quitter slacks off alone, whereas work to rule actions are applied across organized groups of employees under similar work conditions. The Wall St. Journal is willing to tell you about the former behavior, which is youth-coded and unlikely to result in improved conditions, but is not in a hurry to tell you about traditional forms of collective labor action.
On who is allowed to graduate
There is a convention I’ve seen at several institutions whereby a PhD (usually) student who already has a job or post-doc lined up is permitted to defend a dissertation that is less complete than would otherwise be accepted were they not up against a deadline. One suspects this sort of thing is applied in a rather biased fashion, but let’s suppose it was not. I cannot see any justification for it. It produces poor science, it is bad for departmental morale and espirit de corps, and it doesn’t prepare the student for future success in an environment where their advisor can no longer put a finger on the scale.
Now it is true that advisors or committee members, for whatever reason, occasionally try to squeeze a student for more one more experiment that is more of a nice-to-have than essential to make the argument being made in the thesis, but it is not clear why accepting a sub-par dissertation should be a remedy for it, and why such a remedy should only be available if you have a new job starting in two weeks.
“Natural language processing” is not a proper name
The phrase natural language processing is not a proper name, so there’s no reason for it to be written in titlecase: it should be lowercase, like any other common noun phrase.
“Python” is a proper name
In just the last few days I’ve seen a half dozen instances of the phrase python package or python script in published academic work. It’s disappointing to me that this got by the reviewers, action editors, and copy editors, since Python is obviously a proper name and should be in titlecase. (The fact that the interpreter command is python is irrelevant.)
Markdown isn’t good enough to replace LaTeX
I am generally sympathetic with calls to replace LaTeX with something else. LaTeX has terrible defaults, Unicode and font support is a constant problem, the syntax is deliberately obfuscatory, and actual generation is painfully slow (probably because the whole thing is a big pasta factory of interpreted code instead of a single static library).
But at the same time, I don’t think Markdown is really good enough for LaTeX. Of course one can use Pandoc to generate LaTeX from Markdown notes, and its output is often a decent thing to copy and paste into your LaTeX document. But Markdown just doesn’t solve any of the issues I mention, except making the syntax a tad more WYSIWYG than it would be otherwise. And Markdown is quite a bit worse at one thing: the extended syntax for tables is very hard to key in and still much less expressive than LaTeX’s actually pretty rational tabular environment.
Python hasn’t changed much
Since successfully sticking the landing for the migration from Python 2 (circa 3.6 or so), Python has been on a tear with a large number of small releases. These releases have cleaned up some warts in the “batteries included” modules and made huge improvements to the performance of the parser and run-time. There are also a few minor language features added; for instance, f-strings (which I like a lot) and the so-called walrus operator, mostly used for regular expression matching.
When Python improvements (and they are improvements, IMO) are discussed on sites like Hacker News, there is a lot of fear and trepidation. I am not sure why. These are rather minor changes, and they will take years to diffuse through the Python community. Overall, very little has changed.
On getting fired
I probably shouldn’t say too much about this, but I am genuinely baffled why an extremely well-compensated tech employee would torpedo their career just to tell us that they think girls are bad at math, or that they think a language model is sentient. Even if true, what are the material consequence for these claims? What is the right framework for thinking about this? Is clout worth more than a job (“in this economy”)?