[Air-l] Call for Papers 4S session on Latin American Science
rickduque at yahoo.com
Thu Jan 25 17:40:41 PST 2007
Call for Papers: 4S 2007, Montreal, October 11-13
Latin American Science and Ways of Knowing
Abstracts should be sent by January 30th.
Scientists in Latin America face a variety of
challenges contributing to global scientific
knowledge. Given that scientific ways of knowing
are always enacted within particular contexts this
group of papers asks how challenges to producing
scientific knowledge are negotiated in
Latin America? This includes such factors as the
mediation of language politics, as some languages are
scientifically more powerful than others, the
maintenance of networks, and the communication
channels through which both language and networks are
managed in order to create scientific knowledge within
If you are interested in participating in this panel,
Christina Holmes at cpholmes at dal.ca
Abstracts should be sent by January 30th.
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> Today's Topics:
> 1. Question re Size of Data Set... (Matthew
> 2. Re: Question re Size of Data Set... (Paul
> 3. Conferences in Asia this spring? (Jill Walker)
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> 5. The CIA and Facebook (John McNutt)
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> (smork at itu.dk)
> Message: 1
> Date: Wed, 24 Jan 2007 15:31:44 -0800
> From: Matthew Pearson <mdpearson at wisc.edu>
> Subject: [Air-l] Question re Size of Data Set...
> To: air-l at listserv.aoir.org
> <3001D11C-8CB7-42FD-BECE-9F4EA3BE2A35 at wisc.edu>
> Content-Type: text/plain; charset=US-ASCII;
> delsp=yes; format=flowed
> Hello All:
> This is my debut post to this excellent list after a
> long time spent
> reading lots of good stuff from others.
> I have a question re the size of my data set for my
> project: Is my data set too large, too small, or
> just right?
> I'd very much appreciate any insight/ideas/feedback
> anyone has about
> this. My advisor and I aren't that sure about this
> issue, and I
> haven't been able to discern much about this issue
> from a lot of the
> studies I've read.
> I do realize my question is thus far meaningless
> without knowing
> anything about my project, so here's some more
> I'm doing a close look at one message board
> community--one devoted to
> discussion of a particular college basketball team.
> I've got all
> sorts of things I'm interested in, but my central
> research question
> has to do with the ways that people teach each other
> and learn from
> one another the conventions for discourse in/on the
> message board.
> (I'm also interested in potential emerging genres of
> writing, the
> influence of sports fandom on online literacy
> practices, and perhaps
> even examining issues related to gender (which I
> realize is a pretty
> general thing to say, but I'll keep it at that for
> I've got two main sources of data: both (1) archived
> from the message board, and (2) online
> questionnaires that
> participants/members filled out. My question
> concerns source (1)--
> the archival data.
> I have tons of data archived. I used one of those
> programs to grab all the discussions on the message
> board over about
> a 8 month period of time. Given that this message
> board is a pretty
> busy one and that I'm using a ground-theory approach
> to the data
> analysis, I chose to sample a smaller set of the
> overall data. I
> used an "event sampling" method and, with input from
> posters on the
> message board, chose 5 "big" events around which to
> discussion. I then also chose 5 other events that
> occurred during
> the months I archived discussion that were not
> listed as "big" events
> by anyone who offered their sense of the "big"
> events. I didn't,
> though, choose just those threads of discussion
> related to those
> "big" and non-big events, but rather used those as
> anchoring moments
> in time, and then sampled ALL discussion that
> occurred on those
> dates, and one day prior and one day later. This
> resulted in such a
> large data set that I ended up using only 3 "big"
> events and 3 non-
> big ones, and then sampling for those dates, and the
> days immediately
> around them.
> What I'm left with now is about 4000 individual
> .html pages, some of
> which have fairly detailed threads of discussion,
> with sizable
> individual posts, and also, of course, many of which
> that have
> cursory, short sentences that perhaps look more like
> "chat." This is
> a lot of stuff to wade through, yet it does
> represent only 18 days of
> life on this message board. Thus far I've been
> going through the
> data in separate "passes," looking for answers to
> particular aspects
> of my research question, and it's a daunting thing.
> I know research
> takes a lot of work and time, but I thought it wise
> to get feedback
> to see if I'm going overboard here.
> So does my sample sound reasonable? I'm well aware
> that the way I
> sample will directly impact the kinds of conclusions
> can draw and
> level of rigor folks see in my work.
> Any thoughts? Good sources re this kind of
> methodology? I've got
> Virtual Methods Ed. by Hine, among other sources,
> and haven't seen
> anything yet re sample size. Maybe I missed it
> many thanks,
> Matthew Pearson
> mdpearson at wisc.edu
> PhD Candidate, University of Wisconsin Department of
> Composition and Rhetoric;
> Research Assistant, UC-Irvine Writing Project;
> & Man on the Street
> Message: 2
> Date: Thu, 25 Jan 2007 10:47:53 +1100
> From: "Paul Teusner" <paul.teusner at rmit.edu.au>
> Subject: Re: [Air-l] Question re Size of Data Set...
> To: <air-l at listserv.aoir.org>
> Message-ID: <001a01c74012$114cbfe0$6400a8c0 at Paul>
> Keywords: Study
> Content-Type: text/plain; charset="us-ascii"
> Hey Matthew,
> I'm a PhD student too and I'm going through exactly
> the same problem.
> For me the question isn't so much about the amount
> of data but how are you
> going to use it. How in depth is your analysis going
=== message truncated ===
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