Wikipedia:Labels/Newcomer session quality

Purpose
ORES as a predictive algorithm can already predict the quality of single edits and articles, this project aims to extend that capability to sessions of multiple related edits. Being able to predict session quality paves the way for potential future tools such as automatically detecting promising new editors or edit wars on pages. Of course this idea is not new, since 2014 Snuggle has been trying to detect new editors that may have been bitten by vandal-fighters, but its infrastructure is reliant on pre-ORES technology, and is not easily generalizable. Continuing on that stream of work with ORES we plan to first research newcomer retention.

Investigation 1
This investigation tries to determine the relationship between the  and   predictions of single edits, and the same labels applied edit sessions of newcomers. We don't know how the average, or maximum, or any Machine Learning applied to edit quality could be used to predict session quality? Please find the experiment at https://labels.wmflabs.org/ui/ with campaign name "Newcomer Session quality (2018)". It is currently being conducted on enwiki, but many more wiki's are soon to come.

Labelling Instructions
Each task presents you with a session of edits by newcomers. A session is 1 or more edits that occur less than an hour after the previous edit. A newcomer's session is defined as happening on the same day as their date of registration. You are asked to make 2 decisions about each session.


 * Decision 1) "Damaging/Not Damaging"? Please decide whether or not you would revert all of these edits. That is, if their edits are a mix of damaging and not-damaging then then please use the "Not Damaging" label.
 * Decision 2) "Goodfaith/Badfaith" please decide whether you think the author was trying to contribute productively.

Progress
(get detailed statistics directly from the server)