Yes, we are still measuring identifications that are made ‘blind’ - ie IDs that have been made through the https://www.inaturalist.org/pages/identification_quality_experiment and they have been somewhat useful in our explorations into reputation modeling.
You can use https://www.inaturalist.org/observations/identify?quality_grade=any&blind=true&place_id=14&taxon_id=47224 (with appropriate place and taxon params) without registering for the experiment, but registering tells us where your expertise lies.
You’re correct that traditional reputation/crowdsourcing models assume that labels are made blind. But its hard to imagine a social network where participants can’t see each other’s IDs. The models we’ve been experimenting with accomodate this by having both ‘worker skill’ and ‘worker trust’ parameters - where the trust parameter is how much you’re influenced by existing ID’s. You can read a paper about some of the reputation explorations we did last year here:
http://openaccess.thecvf.com/content_cvpr_2018/papers/Van_Horn_Lean_Multiclass_Crowdsourcing_CVPR_2018_paper.pdf
Certainly this data from ‘blind’ IDs could help better understand the ‘trust’ parameter. This reputation modeling arch is quite long term if we implement any of it at all though so, if there are any urgent / short term issues with ‘research grade’ or the ‘community ID’, reputation modeling is probably too long term of a solution