I’ll be at the Online News Association (#ONA14) conference in Chicago, IL USA this week. My attendance is being funded by Temple University’s Center for Public Interest Journalism. I’m looking forward to learning lots and meeting a ton of journalists, editors, and web producers. I’m also hoping to make some connections with editors who get and appreciate my focus on Psychology of Information Technology. Somehow my writing isn’t connecting; I need to figure out what I’m missing.
A pesky thing about being a Psychology nut is one’s need to quantify human traits and behaviors. When the Online News Association emailed out a list of #ONA14 attendees, my curiosity soon had me constructing data of the gender distribution. Here were my quasi-scientific methods:
- Sort categories: 1=female 2=male
- Traditional names were designated according to common Western cultural standards. (e.g., “John”=2, “Mary”=1.)
- About 100 names were not immediately identifiable, due to gender neutrality (e.g. “Chris” or “Pat”) or lack of familiarity (e.g., traditionally Asian names). Unknown names were given a “?”
- Each ?-designated name was Internet searched. About 98% were successfully identified via pictures or third-party pronoun usage in reference to the participant. i.e., pictures depicted genders clearly and LinkedIn references with “he” or “she” referring to the subject confirmed subject’s gender.
- About 3 names were unidentifiable via Google, LinkedIn, Twitter and Facebook searches. A Google image search on the first name was then used to identify the common gender associated with the name, and an approximation was made.
- Error rate on this distribution is not determined, but probably lies within 1-3%, so give or take 3-5 males or females on either side.
My many years in IT prepared me for a very male crowd, but I was pleasantly surprised to discover the distribution is pretty even (51% female, 49% male, of 1617 attendees listed in the ONA “attendees list” document):
The count is pretty evenly distributed, so the error rate isn’t all that significant. Plus, we aren’t publishing here, people, we’re simply trying to get a lay of the land. I’ve attached the attendee list with my sorting results here (.csv raw data): ona14_attendees_by_gender. Here’s a more easily-read pdf: ona14-attendees-by-gender
Next I sought to categorize the self-reported data of titles and occupations. E.g., how many people had “editor” in their brief description of their work position? Producer? Or C-suite title? Was this a conference for management or would I be meeting mostly the ground troops? (This list probably does not include any of those who are manning the Midway tables. At least some big news and tech agencies will be sending at least one recruiter or manager for those display areas.)
Titles are hard to categorize as they tend to be very workplace-specific. An “editor” at a small start-up ezine doesn’t wield the same power as an “editor” at a Gannett property (if there are any left, after all the layoffs, natch). But for this exercise, words alone were categorized in their groups. My judgment determined what sounded like an executive position (“c-suite”). I had to make some guesses, maybe 20 or so out of all 1617 attendees. Some examples of job titles are listed below under each category.
Total N (# of attendees)=1617
- a=academic (professor, lecturer, assistant professor, dean, chair, fellow etc) N=122
- c=executive (director, including assistant and associate directors, founders, presidents, heads) N=481
- d=developer (engineers, app developer, technologist) N=51
- e=editor (including assistant and associate editors, etc.) N=394
- j=journalist (reporter, photographer, freelancer, writer, script writer, guest, N/A) N=129
- m=manager (engagement editor, account manager, outreach, sales, project coordinator, social media) N=241
- p=producer (content manager, art director, curator, artist, performer) N=99
- s=student (graduate students, interns, PhD candidate) N=93
Here’s the distribution:
One would expect more journalists to be in attendance, but this conference is an expensive endeavor. I would guess a good amount of those journos are somehow local or funded, like me, to attend. There were more students and academics than I expected. Many of the academics belong to a Journalism department.
There are technically-savvy types spread throughout the pie. I placed most “social media” titles under the “Manager” category, “digital editors” under the “Editor” title, and all “developers” or “engineers” in the “Developer” group. Many of the c-suite “directors” had some sort of reference to digital news or data in their title. We’ll have a generally technically sharp population at the conference. Next year, though, I think the ONA should court more app developers, perhaps making a track just for them. We need to make nice-nice between the journos and the data wranglers if we are to be useful. 51 coder types is a start.
I could comb this data all day, but this is it for now. You can download the .csv document and give it a go yourself. A profession breakdown by gender would be interesting to see. I noticed a lot of the c-suites were males, but that could just be a sexism bias on my part.
Going to #ONA14? Give me a shout on Twitter @PurpleCar or say hey in the comments.