[{"data":1,"prerenderedAt":35},["ShallowReactive",2],{"maker-chiefofgxbxl":3},[4],{"id":5,"name":6,"line":7,"ai":8,"cat":9,"year":10,"points":11,"comments":12,"rec":13,"s":14,"top":15,"kw":16,"maker":22,"domain":23,"tier":24,"attempts":25,"strip":26,"url":27,"mix":28,"praise":32,"objection":33,"sub":34},28840700,"Vision Zero Reporting","Identify car crash editorial anti-patterns using NLP",false,"learning_science",2021,344,205,"debated",-0.08,4,[17,18,19,20,21],"traffic crash reporting","news bias detection","vision zero","editorial framing","nlp","chiefofgxbxl","visionzeroreporting.com","legend",0,"1300000000011230501111000142344155123303","https:\u002F\u002Fvisionzeroreporting.com\u002F",[29,30,15,31],17,20,24,"Interesting NLP application in a focused domain.","Shifts blame onto drivers instead of addressing systemic road design.","journalism analysis",1793991263501]