Citizen science produces highly accurate transcriptions

We reported last week that we were analyzing the 545,000 fields transcribed from our first year of collaborating with our citizen army to tell the stories of and measure the ANZACs. Among the first things we’re looking at is this question: how accurate are citizen transcriptions. If you’re reading this blog post you’re already among the most engaged volunteers, and so you will be thinking “I take a great deal of care with my transcriptions!” However by having our project open to the world we trade off the benefit of openness for the cost of having some less experienced volunteers who may not read the instructions, or take as much care.

So our question is to measure the measurers: how accurate are they, are you?! We need to show you’re doing a job that’s as good as we could achieve with methods traditionally accepted in the academic world for transcribing this form of material, such as employing undergraduate or graduate students.

We have an amazing opportunity to measure the accuracy of transcription in Measuring the ANZACs because our index to the records includes three variables or fields:  first name, surname, and serial number that we see in the data as well and that are being transcribed. So we have a “gold standard” we can compare to. In a lot of citizen science projects the research team have to hand validate a sample of their data (we’ll do a bit of that too).

We measure “similarity” between your transcriptions and the gold standard with a metric called a Jaro-Winkler score that measures the similarity of the “string” (a string is a sequence of letters or numbers). 1 represents complete accuracy, and 0 total inaccuracy.

But to make the scores more concrete consider that Charles and Cjarles have a similarity of 0.91; Gerry Reid and Henry Reid have a similarity of 0.8, and B and Benjamin have a similarity of 0.74. We can still make a lot of use of transcriptions that score as low as 0.7. Note in particular that “B” is an initial and this may just reflect that the paper had “B” instead of “Benjamin”. The transcription is accurate, but the original person writing it down should have put the full name.

Here’s what we found and presented at last week’s meetings of the Social Science History Association: Citizen scientists are creating highly accurate transcriptions on complex forms with messy handwriting. We have a little work to do to compare this to our previous data collection by graduate students, but on first impressions this is of a very high standard.

Transcription in citizen science is relatively new, and it is important to show that it produces acceptably accurate data. We think it has, and we look forward to now proceeding with our substantive analyses with a high degree of confidence in what we’re analyzing. As we continue with our validation of the data we’ll keep you informed. Now, lets get back to Measuring the ANZACs! Transcribe!

Given name Surname Serial number
Mean similarity score to truth (1 = absolute accuracy) 0.98 0.98 0.98
Proportion absolutely accurate 0.87 0.84 0.89
Proportion with similarity score > 0.9 0.95 0.95 0.95
Proportion with similarity score > 0.8 0.97 0.98 0.98
Proportion with > 3 words in string (more likely problematic transcriptions) 0.0029 0.003 0.003

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  1. Testing our aggregation workflows | Measuring the ANZACs - November 29, 2016

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