Domain > snarpdata.org

More information on this domain is in AlienVault OTX

DNS Resolutions

Date	IP Address
2021-05-01	52.217.129.21 (ClassC)
2024-07-21	52.217.18.227 (ClassC)
2024-09-29	52.216.50.45 (ClassC)
2025-03-22	16.15.216.1 (ClassC)
2025-04-25	54.231.194.189 (ClassC)
2025-06-25	16.182.40.93 (ClassC)
2025-07-16	16.15.178.190 (ClassC)
2025-08-07	52.217.162.29 (ClassC)
2025-08-11	16.15.177.195 (ClassC)
2025-09-13	3.5.8.221 (ClassC)
2025-10-01	52.217.142.5 (ClassC)
2026-01-17	16.15.199.38 (ClassC)

HTTP/1.1 200 OKx-amz-id-2: Glh0TYmxFZuXls6yFtV5krYYDI5FmbgpK3JCRehGKKfYNEQloT9R1NkaCTVzGWZ2lgdq6xdHSZQx-amz-request-id: WT8VFKN1TCVCC2NGDate: Sun, 21 Jul 2024 04:46:32 GMTLast-Modified: Mon, 06 Aug 20

!doctype html>html langen-US>head>  meta charsetutf-8>  meta http-equivContent-Type contenttext/html>  title>snarp data/title>  link relicon href_/img/fav.ico typeimage/x-icon>  link relshortcut icon hreffav.ico typeimage/x-icon>   link relstylesheet typetext/css mediaall href_/css/styles.css>  script typetext/javascript src_/js/jquery-1.10.2.min.js>/script>  script typetext/javascript src_/js/animatescroll.js>/script>!--script>  (function(i,s,o,g,r,a,m){iGoogleAnalyticsObjectr;irir||function(){  (ir.qir.q||).push(arguments)},ir.l1*new Date();as.createElement(o),  ms.getElementsByTagName(o)0;a.async1;a.srcg;m.parentNode.insertBefore(a,m)  })(window,document,script,//www.google-analytics.com/analytics.js,ga);  ga(create, UA-48346176-2, humanrightsscores.org);  ga(require, displayfeatures);  ga(send, pageview);/script> -->/head>body>	  nav idtopnav classclearfix>    div classcontainer>    ul>      li>a href#contact idcontactnavlink>Contact/a>/li>      li>a href#working_papers idworking_papers>Working Papers/a>/li>      li>a href#inthenews idinthenews>In the News/a>/li>      li>a hrefbody idhomenavlink>Home/a>/li>    /ul>  /div>  /nav>!-- @end #topnav -->      section idpagetop namepagetop classcontentpg stylepadding-top:172px>h2>a nameabout idabout>About SNARP/a>/h2>    ul classpub>    p>    Levels of state repression and the frequency, severity, and targets of human rights abuses vary spatially within states. However, most previous studies on these topics have only considered repression in the aggregate. This is problematic because it ignores variation in institutional structures and decision-making processes within countries. We introduce a novel approach for measuring subnational levels of repression using machine learning and human coding techniques. We apply hand coding, dictionary-based approaches and supervised machine learning methods to extract and code physical integrity rights allegations from annual country human rights reports. We present preliminary descriptive statistics from a working georeferenced

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Domain > snarpdata.org

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