Seeing threads about people gaming the system to reach a high number of identifications ….
Seeing firsthand, newcomers adding and agreeing to incorrect IDs without realising the impact on the dataset…
Seeing the mistakes amateur identifiers make even when well-intentioned (myself included!)
Hearing experts reluctance to use the iNat GBIF data or participate here due to larger data quality issues…
I’ve been wondering about how this could be bettered…
It makes zero sense to me that for example :-
An entomologist of global standing, a specialist in a particular family that nobody else can even start to ID without access to a museum collection or decades of research…… should have to argue and debate their ID input with any Tom, Dick or Harry who downloads the app, starts taking pictures and is convinced they’ve found an X, Y or Z.
I think the existing dynamic
- discourages more experts from joining
- puts those off who already pitch in their time so kindly.
- costs significant broader community energy
- limits the level of accuracy the AI can reach…
Personally, I’d be in favour of simply empowering experts and disempowering newcomers.
e.g. something like …one expert ID = RG ….three newcomer IDs = RG
I’m sure similar ideas have been floated for a long time here though… but couldn’t dig out this exact point. Can anyone explain to me what stands in the way of this sort of empowerment / what are the arguments against this by the community?
( I took a look at similar threads, but they are very long! And offhand I couldn’t see anything specifically against empowerment. e.g. It would help resolve some of the issues discussed here - https://forum.inaturalist.org/t/agreeing-with-experts-and-research-grade/3718/90 )
For me, all the wonderful elements iNaturalist has to offer…
Attractive and addictive UI and UX …helping people learn more about the natural world….friendly and welcoming community….open source and community focussed ethos… are in no way mutually exclusive from… a degree of empowerment for experts, and a push towards a cleaner, better dataset.