I think the biggest problem is this:
Let's say that we see the proportion of users who set their gender
preference to female falling. Is that because women are becoming less
likely to set their gender preference or because the ratio is actually
becoming more extreme?
Let's say that we see a trend in the messy data. What do we do about
that? Do we assume that it is a change in the actual ratio? Do we assume
that it is a change in the propensity of females to set their gender
preference and there's nothing for us to do? Or do we then decide that it
is important for us to gather good data so that we can actually know what's
On Thu, Aug 28, 2014 at 4:50 AM, Ryan Kaldari <rkaldari(a)wikimedia.org>
On Tue, Aug 26, 2014 at 9:53 AM, Leila Zia
1. We look at the self-reported gender data and
do some simple
+ we will have an updated view of the gender gap problem.
+ we may spread seeds for further internal and/or external research
- If simple observations are not communicated properly, they will
result in misinformation, that can possibly do more harm than good.
- The results will be very limited given that we know the data is
very limited and contains biases.
I would definitely like to avoid spreading misinformation, which is why
I proposed only looking at the percentage change per month rather than raw
numbers or raw percentages. The raw numbers are almost certainly off-base
and would be much more likely to be latched onto by the public and the
media. Percentage change per month is a less 'sexy' statistic, but might
give us better clues about what's actually going on with the gender gap
over time. It would also, for the first time, give us some window into how
new features or issues may be actively affecting the gender gap. But again,
it would only be a canary in a coal mine, not a tool to draw reliable
conclusions from. For that, we need more extensive tools and analysis.
2. We do extensive gender gap analysis internally.
Proper gender gap analysis, in a way that can
result in meaningful
interventions (think products and features by us or the community) requires
one person from R&D to work on it almost full time for a long period of
time (at least six months, more probably a year). In this case, the
question becomes: How should we prioritize this question? Just to give you
some context: Which of the following areas should this one person from R&D
* reducing gender gap
* increasing editor diversity in terms of nationality/language/...
* increasing the number of active editors independent of gender
* identifying areas Wikipedia is covered the least and finding
editors who can contribute to those areas
I think it's very difficult to judge how to set those priorities without
having more data. We know that the active editors number is on a downward
trajectory. Is the nationality/language diversity increasing or decreasing?
Is the gender gap increasing or decreasing? In cases where things are
actively getting worse, we should set our priorities to address them
sooner, but without knowing those trajectories it's impossible to say.
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