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Using Crimson Hexagon's ForSight social media analytics platform, I categorized a small set of tweets and Crimson Hexagon's patented aggregate opinion analysis algorithm classified the proportions of conversation in each category.
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Using the NICHD Study of Early Child Care and Youth Development I categorize a number of risk factors into one of six settings (i.e. demographic, parenting, neighborhood).
I categorized school as a history lesson because it was so obvious that computers were different.
Enter the National Conference of State Legislatures (NCSL), which I categorized as one of the "Other ALECs" in an investigative series published for TruthOut.
After that, I categorized unoccupied buildings as manageable and tried to adopt a take-it-as-it-comes attitude.
In my book I categorized House and Senate primary challenges since 1970.
Each time someone was earning something, that I categorized as earned respect.
I categorized some as the enemy, some as the terrorists and some as criminals.
As I sifted through documents written using language difficult even for native French speakers, I categorized my findings.
I categorized the most recent 400 lies that The Post had documented through mid-November in the same way my colleagues and I had categorized the lies of the participants in our study.
I categorized these cited sentences as either quoted or paraphrased.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com