Why Tech Jargon Is a Barrier
When we talk about diversity in tech, we often focus on hiring. But the gatekeeping starts much earlier; in the language itself. Tech jargon is used as a sorting mechanism, and research tells us who gets to sort it.
Language Is Power: What Bourdieu Told Us
The sociologist Pierre Bourdieu spent much of his career arguing that the way we speak is a form of currency. He called it linguistic capital — the idea that those who can use the "right" language in the "right" context are granted authority, credibility, and access. Those who can't are excluded.
In tech, this plays out constantly. Knowing the jargon determines whether you get taken seriously in a meeting, whether you feel confident applying for a role, whether you can challenge a decision or ask a question without feeling exposed. It effects whether you're perceived as "technical" or "non-technical", a binary that, once applied, is very hard to shake.
When we label someone "non-technical," what are we really saying about their value?
That label describes a gap in shared vocabulary, one that could be closed in an afternoon, if someone chose to explain things plainly. But often, no one does.
Communities of Practice (and who gets left outside)
Researchers Jean Lave and Etienne Wenger described how learning happens through what they called communities of practice: newcomers gradually absorb the language, norms, and ways of doing things from those already inside a field. Over time, they move from the edges to the centre.
But this only works if the community lets you in.
When jargon is used without explanation (in meetings, in job ads, in interviews, in documentation) it works as a sort of test. It sorts people into "gets it" and "doesn't get it" before they've had any real opportunity to learn. The implication is that if you don't already know, you don't belong. And that implication lands differently depending on who you are. And it sucks.
It's Not Gender-Neutral
Tech jargon presents itself as objective and neutral. But it’s not really. Research consistently shows that the linguistic environment of tech (including the language used in job descriptions, documentation, and workplace communication) actively discourages women from applying, staying, and progressing.
Gaucher, Friesen, and Kay's 2011 study found that job ads using stereotypically masculine language led women to perceive those roles as less welcoming, even when they were objectively qualified. Cheryan and colleagues have shown that the ambient cues of a tech environment (including its language) signal belonging or exclusion before a single word is spoken.
And there's an additional cognitive cost that often goes unacknowledged. Steele and Aronson's research on stereotype threat showed that when people are aware they might be judged by a stereotype ("women aren't technical," for instance, a BS steroetype imo) it consumes cognitive resources. You're both managing the fear of confirming what you suspect others already think about you and trying to work out what the concept actually is.
That is exhausting. And we shouldn’t be made to feel this way.
"Why Don't They Just Google It?"
This is the response that derails every honest conversation about jargon. And it makes me want to throw my laptop off a cilff.
First: you have to know what you don't know. Jargon often obscures the gap itself, if you don't recognise that "endpoint" is a technical term with a specific meaning, you won't know to look it up. Second: Googling one term reliably leads to a definition full of more jargon. You’ll just end up in a maze. Lost, confused and angry. Third, and most importantly: asking reveals your outsider status, and that carries social risk. In a room where everyone else seems comfortable with the vocabulary, admitting you're lost feels dangerous.
The emotional labour of constantly translating (of being perpetually two steps behind, of always being the person who needs things explained) is cumulative. It wears people down. And it drives them out of rooms they deserve to be in. It’s stupid.
The Plain Language Case
And clarity is both the kind thing to do, and it’s more effective. The plain language movement has decades of research behind it showing that straightforward writing improves comprehension across all audiences (including technical ones) reduces errors, and builds trust.
Before: "We need to leverage our API integrations to optimise the end-user experience across touchpoints"
After: "We need to connect our tools better so they're easier for people to use"
The second version is not less intelligent. It is not less precise. It is not less professional. It just doesn't require a decoder ring to understand.
When we choose complexity over clarity, we are making a choice (consciously or not) about who gets to participate. And we should be conciously choosing to include everyone.
What We Can Actually Do?
If you're explaining something: introduce the jargon, then immediately define it in plain English. Use analogies. Check in genuinely to see if it made sense.
If you're lost in a room: ask. The confusion is not yours to be ashamed of. The jargon is the problem, not your intelligence.
If you're hiring: audit your job descriptions. Research shows that the language you use in a job ad shapes who applies, long before anyone gets to an interview. At JobFair, this is exactly what we help teams do.
And more broadly: push back on the idea that tech is supposed to be confusing. When we accept that framing, we accept that only certain people get to participate in the digital world. And that’s not okay.
When we accept that tech should be confusing, we accept that only certain people get to participate in the digital world.
References
Bourdieu, P. (1991). Language and Symbolic Power. Polity Press.
Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge University Press.
Gaucher, D., Friesen, J., & Kay, A. C. (2011). Evidence that gendered wording in job advertisements exists and sustains gender inequality. Journal of Personality and Social Psychology, 101(1), 109–128.
Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6), 1045–1060.
Steele, C. M. & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797–811.
