The idea is to remove the social or political problems
from the process.
Everyone in basically any context wants to remove social and political
problems. Ignoring them is not the same as removing them.
Define the goals and feature sets (this is the part of
the process that
requires community interaction), implement and test the changes, review
the
results. The data is the voice of the community.
It's what proves if an
idea
is good or bad.
As I said before, though, there's always some vocal minority that will
hate
To be clear I dont think every small vocal minority needs to be taken into
account and i dont think wikipedians do either. Sometimes people seem to
use the word vocal minority for a majority of users in some class.
change, even when it's presented with data proving
it to be good. These
people should be ignored at this stage of the process. They can continue
to
provide input to future changes, but the data should
be authoritative.
Data does not prove things "good". Data proves (or more likely provides
some support but not proves) some objective hypothesis. Proving normative
claims with objective data is pretty impossible.
That may sound pendantic, but i think its an important distinction.
Evidence should be presented in the form of "This change improved
findability of the edit button by 40% among anons in our experiment [link
to details]. Therefor I/we believe this is a good change because I/we think
that findability of edit button is important". Separating what the data
proves and what are personal opinions about the data is important to make
the "science" sound legitament and not manipulatrd.
> There's not really a lack of
principles, there's a lack of reasonable
> process. What's wrong with change guided by data science? We know the
> scientific process work.
We know its also extremely easy to manipulate, especially when the science
is only done by one party that has a specific objective. It can also be
myopic, concentrating on one factor well ignoring the holistic whole.
Ultimately the usefulness depends on the skill of whomever is doesigning
and conducting the experiments.
The current process is design by a committee
that's comprised mostly of people untrained in the field, with no data
proving anyone's case. Even when there is data it's often ignored in favor
of consensus of the editor community.
Consensus of the editor commmunity is ancedotal data. That data may be
extremely biased and should be evaluated carefully. But it doesnt make
sense to just throw it out totally, particularaly in cases where its the
only data we have. We should also be evaluating why consensus and data are
conflicting. Maybe there are unstudied factors causing the conflict so the
two positions are not mutually exclusive.
--
Bawolff _______________________________________________