OK, so what do you envision as the application for this training? i.e., once your novices have been trained, what data sets they then be using their new skills to label? Who and/or what sorts of projects would benefit from having access to this crowdsourcing potential?
Since you say it wouldn’t necessarily be intended for iNat users specifically, would the purpose be something like that sketched by @cthawley above – as a streamlined way for scientists to train research assistants for a particular project? The idea being that iNat’s body of verified observations would serve as the basis for training, and once the training model has been developed, it could be supplied with a new photo set and adapted with little work to a different group of organisms?
I think in most cases this would require some initial input from the scientists about identification traits rather than expecting users to intuitively figure out the differences.
As an example, take oil beetles (Meloe). There’s a good, concise overview of British species and their differences here: http://johnwalters.co.uk/research/oil-beetles.php
These are large, distinctive beetles which tend to be somewhat “underlabelled” compared to many other beetle groups. There are only a handful of species in the UK and continental Europe and I suspect, given proper guidance and feedback about identification traits and a suitable set of reliably verified photos, it would be reasonably easy to train a novice to identify the local species.
However, if merely given a set of photos without being told what to look for, I suspect most people would end up frustrated and confused, because they would try to use an obvious trait, like color, to distinguish M. violacea and M. proscarabaeus and would find it difficult to understand why this doesn’t reliably work. Or they might think that the differences in the shape of the antennae mean different species, when in fact this is a sex-based trait.
Meloe also undergo substantial changes in their appearance during the course of their adult life – they expand to at least twice their original length through eating. A novice who has learned to recognize them in their engorged state would (quite understandably) be likely to assume that a freshly emerged adult is a completely different organism altogether. Without an explanation about why the appearance is different, they may resist accepting what the computer is telling them (“the computer must be wrong”).
These factors aren’t unique to Meloe – lots of organisms have more than one form (subadult/non-breeding/breeding plumage in birds, sexual dimorphism, etc.).
Another issue that complicates identification is that the required traits aren’t necessarily always visible in photos because the person taking the photos has to know what to photograph. A lot of the observations on iNat are less than ideal for this purpose, and I suspect that a lot of the observations that are identified may not be correctly identified (see: lack of IDers). So any iNat photos used for training people would probably need to be verified first.
I am not suggesting that the idea won’t work, but these are some factors I see that are important to consider from the outset.
