[Bigbang-dev] research questions of interest for standard-setting participation
Niels ten Oever
niels at article19.org
Fri Feb 16 11:31:22 CET 2018
I would love to at least listen-in!
Cheers,
Niels
On 02/16/2018 01:26 AM, Nick Doty wrote:
> On Feb 5, 2018, at 2:09 PM, Sebastian Benthall <sbenthall at gmail.com
> <mailto:sbenthall at gmail.com>> wrote:
>>
>> 2) I just figured out how to make time for this in the short term. So
>> count me in.
>>
>> Shall we plan a meeting about this?
>
> Yeah, I'd love to do that! Would folks be interested in an audio chat
> next week? I will send around a Doodle poll if it's more than just me
> and Seb.
>
>> On Feb 5, 2018 4:24 PM, "Sebastian Benthall" <sbenthall at gmail.com
>> <mailto:sbenthall at gmail.com>> wrote:
>>
>> These are great questions, Nick.
>>
>> I'd love to work on them with you, especially because they are
>> such general metrics.
>> Sadly I've got almost no time to work on it until May, due to
>> dissertation work.
>>
>> Let me provide some recommendations based on my attempts to
>> address similar questions on SciPy and other lists.
>
> These comments are really helpful, thanks!
>
> I am interested to understand the math better, and could really use your
> help on that. I definitely get your general point that because there's a
> long-tail distribution in any case, I need to find cases that don't fit
> that pattern in order to show meaningful results.
>
> I'm not sure I understand the concentration parameter, but it does seem
> like something like that would be useful. I also thought there might be
> interesting graph analysis metrics -- like centrality? -- in a graph of
> the nodes of connections between participants and lists.
>
> Thanks again for your thoughts!
> —Nick
>
>>
>> * how many participants total in IETF work?
>>
>>
>> The odds are *very* high that the emails-per-person distribution
>> is a heavy-tail distribution.
>> Based on previous work
>> <https://conference.scipy.org/proceedings/scipy2015/pdfs/sebastian_benthall.pdf>,
>> I would test for fit to log normal and power law distributions.
>> My money is on log normal being a better fit.
>>
>> This is important because when interpreting the results, we have
>> to keep in mind that
>> the log normal distribution is essentially a noise pattern.
>> So it's easy to read into the data relationships that may not be
>> there,
>> especially if you're using a linear rather than a log linear
>> relationship as an indicator.
>>
>> * how "sticky" is participation?
>> if people participate on a list, do they return? do
>> they show up to f2f meetings?
>> what's the attrition rate?
>> what's the distribution of length of participation?
>>
>>
>> Assuming there is a heavy tail distribution of participation, then
>> about half the contributors
>> will only contribute once.
>>
>> The distribution of attrition/retention will look more or less
>> just like the distribution of participation.
>> The length will look like it as well.
>>
>> It's not clear how to interpret this, because the reasons why any
>> particular person participates a lot
>> or a little are very likely
>> (a) myriad (no single reason, but rather a combination of many
>> reasons, and
>> (b) exogenous to the data itself.
>>
>> For these reasons I expect you would get more interesting results
>> if you can segment the population
>> into categories of interest. You've mentioned gender and firms of
>> employment, which are both good ones.
>>
>> But for each category, you may want to have more than one parameter to
>> characterize the each one's participation distribution.
>> May mean /and/ variance?
>>
>> * who has participated longest? across the most groups?
>> is there a group of "elites" across working groups?
>>
>>
>> This is a great question.
>> But keep in mind: the people who participate most are going to be
>> participating a lot
>> more numerically across all lists than others.
>> So they will have more chances to participate in different lists.
>>
>> You may want to be looking at, for each participant, their
>> individual distribution of participation
>> over many lists, and then look at the concentration parameter of
>> that distribution:
>>
>> https://en.wikipedia.org/wiki/Concentration_parameter
>> <https://en.wikipedia.org/wiki/Concentration_parameter>
>>
>> The math can be a bit tricky but I think it's worth tackling
>> correctly.
>>
>>
>> how many participants are single-group?
>>
>>
>> Since most participants will be only send one message, that's
>> going to skew this metric
>> unless you take that into account somehow.
>>
>>
>> how many groups does the typical participant join?
>>
>> As I believe I've mentioned to this group before, I've been
>> looking into estimating gender in mailing list participation,
>> including:
>>
>> * What is the gender distribution of participants in Internet
>> and Web technical standard-setting?
>> how does that distribution differ from the population at
>> large? from employment at related firms?
>> does that distribution change over time?
>> are there sub-groups which have distinctly different
>> distributions?
>> * Does the gender distribution of conversation differ from the
>> gender distribution of the participants?
>>
>>
>> Great questions.
>>
>>
>> Do you have questions you'd like to add to this list? Would
>> you be interested in trying to measure/answer one of these
>> questions? Which are the easiest and which are the most
>> difficult? What features would we need to add to BigBang to
>> make them answerable?
>>
>>
>> In sum, I think all these questions are great ones and related to
>> each other.
>> I think the biggest challenge is getting the correct statistical
>> modeling right,
>> so that the results are not misinterpreted.
>>
>> - Seb
>>
>>
>
>
>
> _______________________________________________
> Bigbang-dev mailing list
> Bigbang-dev at data-activism.net
> https://lists.ghserv.net/mailman/listinfo/bigbang-dev
>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: signature.asc
Type: application/pgp-signature
Size: 833 bytes
Desc: OpenPGP digital signature
URL: <http://lists.ghserv.net/pipermail/bigbang-dev/attachments/20180216/c052cc11/attachment.sig>
More information about the Bigbang-dev
mailing list