Open nniiicc opened 1 year ago
@nniiicc Here is the script that I found in the blacklight-collector repository that might doing the counting work https://github.com/the-markup/blacklight-collector/blob/main/src/parser.ts#L45 And I believe here is where they fit in data https://github.com/the-markup/blacklight-collector/blob/383eb2fbafa5e17585c2f0b1e6976cade91e3ecf/__tests__/parser.ts#L15
Here is the JSON file the black light collector produce https://github.com/peiwenf/campaign-access-eval/blob/main/inspection.json And this is the result I got for the same link on their website, which is surprisingly different. My guess is that the website only scans the base page while the json file contains three subpages. https://themarkup.org/blacklight?url=reg.recreation.uw.edu
Here is the script that Tor Browser Friendliness project calculating their score https://github.com/askumar2903/tor_browser_friendliness/blob/main/src/score-calculation.ts
And here is the link to the drop box folder I created https://www.dropbox.com/scl/fo/4619d5qiknngk0njjgbgj/h?dl=0&rlkey=g3s6xbmu2i6nl3b7r5fb5kvgu
@nniiicc Here is the black light collector reports generated by the Github Action, which are three examples from the attorney general data frame
And here is a description for the JSON file
I believe we are most interested in the inspection.json
Questions about the next step:
Given a list of URLs from political candidates we want to
There is a command line tool we can use to develop reports for political candidates