Ladies And Gentlemen, Our Language Is Too Gendered

Hey guys, can't we just call one another "people"?
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Ladies and gentlemen, boys and girls. The words we use to refer to each other collectively have a tendency to lump us into gendered groups.

The choice to identify people ― both strangers and close friends ― by their genders has long been a go-to for English-speakers. But how did this practice start, and is it still relevant to how we live today?

The Toast has an excellent breakdown of how gendered language first appeared thousands of years ago. Briefly, in Proto-Indo-European English, there were two grammatical “genders,” or classes of nouns and pronouns: animate and inanimate. “Animate” later split into two more genders, male and female. Some of the languages that emerged ― including Armenian and Danish ― later lost the male and female divide.

In English, the divide remains clear, evidenced by a poll conducted by The Huffington Post and YouGov.

Respondents were asked how they would refer to two groups: women under age 20 and men under age 20. They were given the option of referring to these groups as simply “people,” but fewer than 2 percent selected that choice. “Guys” was also presented as an option regardless of gender (as in, “hey, guys”), but poll-takers were reticent to refer to a group of women as “guys,” too ― it only accounted for 1 percent of the vote.

Instead of those gender-neutral terms, 41 percent of respondents said they would refer to a group of women under 20 as “young women”; 33 percent would refer to them as “girls”; 9 percent would refer to them as “ladies,” and 4 percent would refer them as “chicks.” Thirty-six percent of poll-takers said they would refer to an all-male group under 20 as “boys”; 38 percent prefer “guys,” and 6 percent prefer “gentlemen.”

The same trend arose from questions about men and women over 20. Forty-two percent of respondents would call women over 20 “women”; 31 percent would call them “ladies”; 12 percent would call them “young women,” and 6 percent would call them “girls.” Thirty-one percent of poll-takers would refer to an all-male group over 20 as “guys”; 45 percent would refer to them as “men.” Again, fewer than 2 percent of respondents to both questions would refer to the group as “people.”

Guys and chicks, men and women. These words don’t account for gender non-conforming people, and even for those who identify as cis-gender, they place our maleness or femaleness at the forefront of our personalities. Is it time to consider moving away from those swift categorizations we make in English?

With singular pronouns ― him and her ― there’s an alternative that’s arisen, one that publications as widely read as The Washington Post have embraced. The singular “they,” while considered grammatically unsound by some linguists, is gaining traction as a gender-neutral alternative.

But when referring to collective groups, the YouGov poll shows, we often default to gendered categorizations in spite of the host of available alternatives, including “group,” “tribe” and “crowd.”

Maybe we, the people, could stand to rethink that.

The HuffPost/YouGov poll consisted of 1,000 completed interviews conducted Sept. 17-Sept. 19 among U.S. adults, using a sample selected from YouGov’s opt-in online panel to match the demographics and other characteristics of the adult U.S. population.

The Huffington Post has teamed up with YouGov to conduct daily opinion polls.You can learn more about this project and take part in YouGov’s nationally representative opinion polling. Data from all HuffPost/YouGov polls can be found here. More details on the polls’ methodology are available here.

Most surveys report a margin of error that represents some, but not all, potential survey errors. YouGov’s reports include a model-based margin of error, which rests on a specific set of statistical assumptions about the selected sample, rather than the standard methodology for random probability sampling. If these assumptions are wrong, the model-based margin of error may also be inaccurate. Click here for a more detailed explanation of the model-based margin of error.

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