If you've been watching this series, you'll know I care about data. But data has its limitations, especially when it comes to language. Basically, if you get your categories wrong, you can wind up with some pretty misleading statistics, and the US Census is a prime example.
如果你一直有在看這系列, 就會知道我很在意資料。 但是資料有其限制, 特別是在用語這方面。 基本上,如果弄錯了類別, 結果可能是非常誤導的統計資訊。 美國人口普查就是最好的例子。
[Am I Normal? with Mona Chalabi]
〔我正常嗎?〕 〔夢娜查拉比主持〕
Taken every 10 years, this survey aims to collect demographic data from each and every resident of the US and its territories. Those responses help the government to determine everything, from the allocation of seats in Congress and the Electoral College, to the allocation of hundreds of billions of dollars in federal funds. And those funds pay for things like new hospitals, road improvements and school lunch programs. And crucially, the statisticians that work there are nonpartisan. They sit at the same desks, applying the same formulas, no matter who is in charge at the White House.
每十年進行一次, 這項調查旨在收集美國及其領土上 所有居民的人口統計資料。 調查的回應能協助政府 決定各種事項, 從國會和選舉人團席次安排, 到數千億美元聯邦資金的分配運用。 這些資金可以運用的地方包括 建造新醫院、道路改善, 和學校午餐計畫。 重要的是,在那裡工作的 統計學家都是無黨派的。 他們坐在同樣的桌子前, 套用同樣的公式, 無論白宮換誰當家。 所以,無疑地,美國 人口普查局做的工作很重要,
So undoubtedly, the US Census Bureau does important work, but it does have some blind spots. For example, there has been a decades-long effort to add the category Middle Eastern or Northern African or MENA to the census. Currently, the census defines people from these regions, and that includes me, as white. Yeah, that's incorrect. In 2015, the census did test a version of this survey that included MENA. It found that when given the MENA option, the number of people from that region who identified as white dropped from 86 percent to 20 percent. See, when you reconsider language, the numbers can change dramatically. Unfortunately, though, the census still didn't make the change, saying that further tests were necessary to determine if MENA should appear under ethnicity instead of race. That means that those who have rallied for its inclusion will have to wait another decade to see if our community can be recognized.
但它還是有盲點。 比如,數十年來一直有人努力 要在普查中納入「中東或北非」 (或稱 MENA)的類別。 目前,普查局把這些地區的人定義為 白人,包括我在內。 是的,我不是白人。 2015 年,普查局的確測試了 一版有納入 MENA 的調查。 調查發現,有提供 MENA 選項時, 該地區被認為是白人的人, 從 68% 減少到 20%。 看吧,重新考量用語, 就可能讓數字大不同。 不幸的是,普查局 並未因此做出改變, 說法是,還需要進一步測試 才能決定是否要把 MENA 放在族群底下而非種族。 那就表示,那些團結起來 希望被納入的人還得再等十年 才能再看看我們 這個族群是否能被承認。
This isn't the first time that language has restricted how people are represented in the census. The very first one, way back in 1790, only had three broad categories, and I quote: "slaves, free white men and women, and all other free persons." It would be another 30 years before distinct categories for free Blacks and another 40 years before American Indians would appear on the census.
這並非頭一次用語限制了 人民在普查中被代表被呈現的方式。 最早的調查可回溯到 1790 年, 當時只有三個很廣的 分類,引述如下: 「奴隸、自由的白種男人及女人, 及所有其他自由人。」 再過三十年後, 才多出自由黑人的明確類別, 又再過四十年之後, 美國印地安人的選項才出現。 從那之後,越來越多 類別被納入普查。
Since then, more and more categories have been added, but progress has been slow. It wasn't until 2000 that people could choose more than one race to describe themselves, and for the very first time in 2020, people who selected Black or white could go a bit more granular and provide more detail about their origins, like naming France or Somalia or spotlighting their Indigenous identity.
但進展相當緩慢。 要到 2000 年,人民才能選擇 用不只一種種族來描述自己。 2020 年,史上第一次, 選擇黑人或白人之後,可以再更細部 提供關於其血統的細節, 如指明法國或索馬利亞, 或強調其屬於原住民哪一族。
Right now, you might be thinking: Why does the wording on a survey even matter? Race and ethnicity are social constructs anyway. But that doesn't change the lived experience of those who aren't truly reflected in these forms. Questionnaires need to ask the right questions if they want to capture what's really happening in the world. A Northern African non-binary person might be misgendered or considered white by the census, but face disproportional discrimination, health disparities or language barriers that are unique to their community. It's no wonder, then, that it's often marginalized and vulnerable communities ones whose identities are missing from these forms that lack access to governmental resources and protections.
現在,你可能在想: 調查的用詞有什麼重要的? 反正種族和族群都是社會構面。 但對於沒有被表格 真實反映出來的人, 他們的生活經歷也不會有所改變。 問卷必須要問對問題, 才能了解世界上真正發生的狀況。 非二元性別的北非人可能 在普查中會被誤植性別 或被視為白人, 但卻仍然會遇到只有他們的族群 才要面對的不成比例歧視、 健康不平等,或語言障礙。 也難怪通常這些被邊緣化 和弱勢的族群, 這些表格中沒有選項可選的族群, 會很難取得政府資源或保護。 有一些可理解的歷史性理由可以說明
Now, there are some understandable historical reasons why people might not want to engage in this kind of data gathering. But without the data, it’s just easier to deny the inequality is real. If we want a more equitable society, we have to measure our reality, and the best way to start is by using language that recognizes our differences.
為什麼沒有人想要 參與這類資料收集。 但若沒有這些資料, 就更容易否認不平等是真的。 如果我們想要有更平等的社會, 我們就得測量我們的現實, 最好的第一步, 便是採用能識別我們之間 差異的用語。