Every semester, there are three weeks that are absolute hell: O-week to the end of week 2. Week 3 is busy, but less stressful. Every year, I make sure I see my friends before O-week starts, and then tell them I’ll see them again in a month.

This blog has had the same problem. It’s not that I haven’t been doing anything, but it’s been out of synch, late, or patchy.

I’ve updated the things I was supposed to have done, or had started but not published, in week 8 only this weekend, as we turn the corner into week 9. I managed a couple of tweets in the tweetorial (and neither of them had space for the hashtag, #fail).

I did have a fascinating conversation with Nicholas and Jeremy earlier:

https://storify.com/katrinafee/foucault-and-algorithms

I had trouble finding an algorithim I could play with, so after a few abortive attempts, I finally worked out I could look at Twitter’s Discovery timeline. I commented on Jin’s and PJ’s bogs.

Posting less often means that some of my posts have been longer. I’ve been continuing to document the places where I study (and increasingly, the food I eat as I study), including my first video:

Anyway, week 3 is coming up. Busy, but no longer crazy. Hopefully that means I’ll have a chance to catch up.

Gillespie explores in more detail the reason that Knox (2014) is a useful insertion into the field. Algorithms are broadly understood as, and presented as, objective and data driven. However, algorithms are actually crafted by software engineers, and are likely to emphasise their world views as to what is important, and what counts.

Gillespie (nd) p. 2

Gillespie (nd) p10

Gillespie (nd) p. 12

For example, monetisation, popularity, or scientific evidence (see recent changes to Google for health searches), which are Western, capitalist and post-Enlightenment values. Other voices may be hidden because the algorithm or software doesn’t even count it–the new Health app on all iPhones was launched with no way to track menstruation, even though this is a very common aspect of women’s health. This suggests male is another human bias baked into many algorithms. Gillespie mentions other such biases, such as Amazon and YouTube ignoring ‘adult’ or ‘suggestive’ (i.e. with sexual content) works in their rank (p.5-6).

Algorithms are not exhaustive knowledge systems, but fast heuristic devices, where quick, good enough, judgements are preferred. This has the effect of privileging norms and majorities, and therefore increasing their significance.

What is being posted on the web is strongly influenced by the algorithms. I continue to be part of various Social Media / Community Manager online communities. Recently, Facebook started to prioritise ‘native video’ (ie video posted directly to the site, rather than embedded from another site). They are apparently strongly promoting posts with native video, meaning that where 4 years ago your most effective posts had a picture (as they were promoting Facebook as a visual platform) now they will have a video.

Content managers and marketers are therefore out shooting video where we used to go out to shoot pictures. Their daily actions and tasks are changing. This also changes the way we look at the world. I often framed things I saw as I walked around campus as candid phone shots, or later framed and filtered Instagram pictures. Looking for video is a different way of judging what we look at, literally a different way of looking at the world. (See p.20).

***

I made a video with Instagram to reflect the ways in which the above might play out in digital media.