This Post Is About (My) Gender Identity And Nothing Else

I'm back, fuckers.

In this yearly edition of posts celebrating this blog's birthday which almost always immediately derail into depressive rants, I'm going to be explaining gender identity, so I have something to reference back other people to when they somehow find out I use they/them pronouns, ^{(1) (1)} I only tell my friends this, and normally they don't need explaining to for things like this. Also yes, since I've just told you, that means we're friends. and want to know "why?"

Although I probably won't show them this post because I literally opened it with calling you all "fuckers," and that's a bit mean.

Anyway we're going to take on a bit of a detour first as to understand gender, we must first understand the economic supply and demand curve.

What's often called "Economics 101" and used as the basis for many Internet arguments I see, is just that - "101," the introductory course, a simplification for something much more complex.

Consider the fundamental causality problem. The model first asks "At price P, how much will firms produce?" suggesting price determines quantity. But when explaining market dynamics, it suddenly flips to say excess quantity drives price changes. This is like having a circular equation where P = f(Q) and Q = g(P) simultaneously, which creates a logical paradox. For example, if we say a firm will produce 100 units at $5, but then that 100 units causes the price to drop to $4, what was the actual relationship between price and quantity?

The inconsistencies become even clearer when we model more realistic firm behaviour. Take our earlier example with two firms and a demand curve P = 17 - (Q1 + Q2). ^{(2) (2)} Where 17 is the maximum price consumers would be willing to pay when the total quantity in the market is zero (Q1 + Q2 = 0). This is often called the "choke price" or the "reservation price" in economics. You can think of it as the y-intercept of the demand curve. If we ask "What quantity would you produce at $5?", Firm 1 might say 9 units and Firm 2 might say 8 units. But these quantities would drive the price to:

P = 17 - (9 + 8) = 17 - 17 = $0

This forces both firms to recalculate their quantities, leading to a different equilibrium, perhaps:

Q1 = 6
Q2 = 5
P = 17 - (6 + 5) = 17 - 11 = $6

These equations show how market concentration affects prices in ways the simple supply-demand model can't capture. When concentration is small, firms have significant price-setting power, invalidating the assumption that they're "price takers."

External factors further complicate the picture. Government regulations like price floors or ceilings create kinked demand curves. Taxes shift curves in complex ways - a $1 tax doesn't simply shift the supply curve up by $1 because it changes the underlying production decisions. Economic cycles can cause both curves to shift simultaneously, making it impossible to isolate individual effects.

Price elasticity adds another layer of complexity. The model draws straight or gently curved lines, but real demand curves can have sharp kinks and varying slopes. Essential goods like insulin have nearly vertical demand curves at certain quantities, while luxury goods might have multiple elastic and inelastic regions.

None of this is to say that the supply-demand model should be "abolished," it's still a useful model and there's a reason it's taught. ^{(3) (3)} Being easy to understand is one of them. But the key is understanding when its simplifying assumptions are appropriate and when more sophisticated models are needed.

It's like how we still use Newtonian physics when modern physics is a much more accurate model of the universe. Newton's laws of motion and gravity work remarkably well for everyday calculations - from launching satellites to designing buildings to calculating the trajectory of a baseball. Engineers and architects still rely on these equations because they're simple, intuitive, and provide extremely accurate results within the scope of normal human experience.

It's only when we look at the very small (quantum scale), the very fast (near light speed), or the very massive (astronomical scale) that Newton's equations start to break down, and we need Einstein's relativity or quantum mechanics. For instance, Mercury's orbit couldn't be fully explained by Newtonian gravity, and GPS satellites need to account for relativistic time dilation to maintain accuracy. But just as we don't need quantum mechanics to build a bridge or relativity to calculate how long it takes a car to stop, we don't always need complex economic models to understand basic market behaviour.

However while useful, simplified models are still technically "incorrect". And the point I'm trying to make is that "correct" (or rather as correct as we know so far) is generally much more complicated than what you were initially taught in high-school.

Most areas of study are more complex and less "figured out" than what you would initially learn. Let's look at another example - the binary categorisation of sexes. No I'm not talking about gender (we're still not up to that), I'm talking about biological sex - body parts, hormones, genetics, etc.

While we traditionally think of sex as XX = female and XY = male, the reality is more complex (as it often is). There are XY people who can give birth and XX people who produce sperm, along with many other variations in genetics (XXY, XYY, X, etc.) and hormonal responses.

When scientists plot all these biological sex characteristics on a graph, they get what's called a bimodal distribution - two major peaks (traditionally labelled "male" and "female") with a range of variations between and around them. Historically, we've oversimplified this by forcing everyone into the two majority categories, but this approach is becoming problematic for medical research and treatment. For example, different groups respond differently to certain medications and environmental factors like dioxins, making it important to recognise these biological variations.

Again we're not talking about gender or transgender identity, but purely about biological characteristics. Like in the other fields we talked about, this probably doesn't matter for your day-to-day life and the simplified binary model is "mostly" fine since someone's sex really shouldn't matter to you, but it is incorrect, and for the scientific community, treating sex as a binary rather than a spectrum hinders scientific understanding and medical treatment. This isn't a new or ideological position - it's simply what the biological data shows when we look at it carefully.

For some extended reading, the book "Brain Gender" (2005) by Melissa Hines explains how brain structures don't follow a strict male/female binary. Instead, each brain region contains varying degrees of traditionally "male" and "female" characteristics. The paper - Joel D, McCarthy MM. (2017) "Incorporating Sex As a Biological Variable in Neuropsychiatric Research: Where Are We Now and Where Should We Be?" similarly discusses variation in brain structure beyond simple sex differences.

This paper on genetic complexity - Gregg C, Zhang J, Weissbourd B, Luo S, Schroth GP, Haig D, Dulac C (2010) "High-resolution analysis of parent-of-origin allelic expression in the mouse brain" used mouse brains to examine how genes are expressed differently depending on whether they came from the mother or father (genomic imprinting). By analysing both embryonic and adult mouse brains, they found over 1,300 locations where parental origin affects gene expression, with maternal genes being more active during brain development and paternal genes dominating in adult brains. While conducted in mice, this research reveals how genetic expression is more complex than simple XX/XY chromosomes, as genes can be activated differently based on their parental origin, developmental timing, and location in the brain.

And Springer KW, Mager Stellman J, Jordan-Young RM (2012) "Beyond a catalogue of differences: a theoretical frame and good practice guidelines for researching sex/gender in human health" takes a broader view, critiquing how sex differences are researched in human health. They argue that simply cataloguing differences between males and females is insufficient and potentially unscientific, suggesting instead that researchers need a more sophisticated theoretical framework for studying sex and gender in health contexts.

What's notable about these texts is that they come from different angles - neuroscience, genetics, and public health - yet all point to the same conclusion: biological sex is more complex than a simple binary, and treating it as such has real implications for medical research and treatment.

Right with that very important background information, let's get back on track. You came here for my relationship with a certain contentious concept. That concept of course being - programming language design.

You may remember an interesting paper that made the rounds in programming circles a few months ago - Hermans, Felienne and Schlesinger, Ari (2024) "A Case for Feminism in Programming Language Design." It's an interesting read that you should go through yourself, but I want to talk about what I took from it.

Considering modern feminism has moved past from being solely about women's rights and into a study of power dynamics in society, I thought this paper would be about exploring power dynamics in programming languages (i.e. what do we allow and forbid the user to do, how that affects the end result and whatnot) through the lens of feminism. But it's actually more of a demographic study of the programming language community and examines how the demographic makeup shape languages in certain ways (most of them bad, as the paper argues).

Which in my opinion is a little less interesting, but that's fine, maybe someone else will do the former at some point in the future. ^{(4) (4)} It won't be me though. I know one of you fucks will ask.

Anyway, the grounds of the feminist critique are what we could call a critical history - an approach to studying the past that fundamentally differs from traditional historical analysis. Rather than simply documenting what happened, critical history examines how our ways of thinking and understanding have developed over time. It suggests that what we consider "knowledge" or "truth" in any period isn't universal or timeless, but is shaped by the social, political, and cultural forces of that era.

Think of it this way: When we study calculus today, it seems like a set of objective mathematical truths that were discovered rather than created. A critical historian would instead examine how calculus emerged from specific needs and thought patterns of 17th century Europe, how its development was influenced by the philosophical and religious beliefs of the time, and how the way we think about it now might be different from how it was originally conceived.

This approach questions the idea that knowledge and reason develop in a straight line of progress toward greater truth. Instead, it suggests that what we consider rational or logical thinking is itself a product of particular historical circumstances. Traditional history might tell us what Newton discovered; critical history asks us to examine the entire framework of thinking that made Newton's discoveries possible and shaped how they were understood.

Taking this critical history approach to programming languages, we can examine how the field's foundational assumptions emerged from specific historical contexts - exactly what the article's author encountered with their work on spreadsheets.

The paper explains how the programming language community socially constructs what counts as a "real" programming language. Despite spreadsheets being Turing complete (technically capable of all computation), they were consistently dismissed as "not real programming languages." Meanwhile, the definition remained fluid for other technologies - Python evolved from being considered "just a scripting language" to a "real" programming language, and UML appears in books about programming language history despite its contested status.

This reveals how the boundaries of what constitutes a programming language aren't determined by purely technical criteria, but by social power structures within the academic and professional computing communities. This gatekeeping particularly affects early-career researchers who work on "non-traditional" programming languages - they can't participate in the programming language research community because their work has been defined as outside the field's scope.

It also questions why programming language research heavily favours formal proofs over human studies, despite both approaches being valid in the related field of software engineering. This methodological preference isn't a natural or inevitable choice - it reflects specific values and assumptions about what constitutes valid knowledge in the field. While software engineering embraces a range of research methods from formal proofs to qualitative studies and user observations, programming language research remains more narrowly focused.

These patterns reveal how the field of programming languages isn't just a set of technical tools, but a social construct shaped by historical circumstances and power structures. The paper argues that feminist theory provides useful tools for analysing these structures because feminism has extensive experience studying and challenging similar systems of power in other contexts - from voting rights to financial access. This doesn't invalidate the technical achievements in programming language design, but rather provides a framework for understanding how social and historical contexts have shaped what we consider valid or valuable in programming language research and design.

Now, swap out "programming language research" for "everything" and you got your answer to the question of this blog post. You're welcome, I'm glad I could help :)

I think that's all for today, see you next year.

Epilogue

I don't really have any to say about my 5 month absence on this website, I think I've warned you enough last year that posts will become rarer. They will come out when the come out, but you're guaranteed at least one a year (this one).

Anyway, have you played Titanfall 2? Holy smokes that game fucking rocks. I thought I wouldn't like it because I didn't like Apex Legends and surely my obsession with Ultrakill would make me hate any other movement shooter as no other could compare to that Finnish masterpiece.

But no, it's fucking incredible. The schmovement. The set pieces. The shooting. Like holy shit. It's so good.

My only complaint is that embarking into your titan is too context-sensitive. Like you have to walk up to the guy, find the right spot, and press E. I want to fucking wall ride and jump into that guy's face and make him catch me or something, that'd be sick. It really kills the flow without something like that.

And I'm not joking - that is my ONLY complaint, literally everything about the game is perfect.

But yeah I originally played it because Deadlock is pretty much dead right now in Oceania, and I really needed to fill my movement shooter fix. I played like one game of Marvel Rivals and uninstalled because it felt like complete shit to play, ^{(5) (5)} It seems like a lot of people like it though. I dunno how to explain why I don't. So I'll just say if you're one of the sick fucks (like me), who bind "jump" to a mouse button, you'll feel the same way. It also probably didn't help that I played it after a hundred hours of Deadlock - a game I now consider the best competitive multiplayer game to ever exist, and it's still an invite only pre-alpha. so I looked into my backlog and found my new saviour.

Also on the Steam cover art of Titanfall 2, there's a titan wall running, but like… that never happens? You can't jump in the titan, so it can't wall run. Actually I have a second complaint and it's that. I want titan schmovement. And it should be EXACTLY like Armored Core VI.

So Respawn Entertainment, now that Apex Legends is dying, it's time to make Titanfall 3.