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.
如果你有看我们这一些系列节目, 你就知道我非常重视数据, 但数据有其局限性,尤其在语言方面。 基本上,如果你一开始把类别搞错, 你最后就会得到错误的数据。 美国的人口普查就是个典型的例子。
[Am I Normal? with Mona Chalabi]
【我正常吗? 一起听听莫娜·沙拉比】
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.
美国人口普查,每 10 年举行一次, 目的是为了收集人口统计数据, 有关每一位美国及其领土内公民的数据。 这些数据能帮助政府进行决策, 从国会和选举团的席位分配 到上千亿联邦财政资金的分配。 这些财政资金会分配到不同的项目, 比如,新医院建设、 道路改善和学校午餐计划。 重要的是,在那里 工作的统计学家是无党派的。 他们坐在同一张桌子旁, 套用同一个公式, 无论在白宫掌权的是谁, 他们的工作都是这样。
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.
毫无疑问, 美国人口普查局的工作非常重要, 但他们的工作存在一些盲区。 举个例子,数十年来他们一直在努力, 希望在人口普查中增加中东或北非, 或中东北非的人口类别。 最近,他们将来自这一区域的人, 定义为白人,包括我。 当然,这是错误的。 2015年,人口普查尝试了在调查中 增加“中东北非”’的人口类别, 结果发现当有了“中东北非”的类别选项, 来自这个区域的人 认为自己是白人的比例。 从 86% 降到了 20% , 你看,当你重新考虑到语言的问题, 数字就会发生巨大的变化。 但令人遗憾的是, 人口普查局最终没有增加选项, 他们认为,是否要增加“中东北非”的类别, 用种族而不是人种来划分人群 还需要进一步的测试。 这就意味着,那些对自己种族归类有异议, 并为此集会发声的人 需要再等十年, 再看他们的种族群体是否会被认可。
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.
在人口普查中, 这不是第一次因为语言问题, 而限制了人口群体的归类方式。 早在 1970 年,就发生了这样的问题, 那是只有比较泛的三大类, 我引用当时的分类,就是 奴隶、自由的白人和妇女,以及其他自由人。 30 年之后, 有了自由的黑人的类别, 40 年之后,又有了美洲印第安人的类别 出现在人口普查中,
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.
从那以后,越来越多的人口类别 添加到人口普查中, 但是整个进程还是很慢。 直到 2000 年,人们才可以选择 一个以上的人口类别选项, 来代表他们的种族身份。 并且在 2020 年,人们第一次 可以在选择黑人或白人选项后, 有更细化的选项。 以及可以提供一些细节信息, 来补充他们的种族身份。 比如,可以直接写法国或索马里, 或者突出他们的土著身份。
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.
现在,你可能会想 为什么调查中的用词会这么重要? 人种和种族不都是社会建构的一部分吗? 那些没有被人口普查正确归类的人们, 他们的生活并没有因此有什么变化, 但调查问卷应该问正确的问题, 如果他们想要得到最真实的信息, 一个来自北非的, 不能用二元性别区分的人, 在人口普查中可能被错误归类性别, 或者被归类为白人。 这就造成了对该类种族群体独有的 不成比例的歧视、 健康差异和语言障碍。 这也难怪, 这些经常被边缘化。 在人口普查中也没有 明确的种族身份的弱势种族群体, 无法获得政府的资源和保护。
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.
现在,一些人不想参加这种数据收集调查 是有一些可以理解的历史原因的, 但是没有数据,想要否认现存的不公平, 只会更加容易。 如果我们想要让社会更公平, 我们必须用数据来展现社会现实, 最好的开始方式 就是用语言来承认我们之间的差异。