Babble Voice Privacy System

Kirsten Jones recently pointed me to Herman Miller‘s Babble Voice Privacy System. Babble is a small device (about the size of a tape player) that takes sound within a certain radius and rebroadcasts it as nonsense. In other words, it allows you to have private conversations in open spaces.    (MQP)

Babble is being marketed as a privacy device, but it’s actually an important productivity device. People are good at ignoring white noise. When our brains hear sounds they don’t recognize, they ignore them. People are bad at ignoring recognizable sounds. Every ambient conversation we overhear is a concentration breaker.    (MQQ)

The list price for a Babble is $695, which is steep for most mortals. However, there’s a simple trick you can use for similar effect: music. People do this all the time on their own: When they need to concentrate, they put on their headphones. However, you can do this for an entire space as well.    (MQR)

The added benefit of using music in this way for Open Space-style events is that you can use this as a transitional device. Raise the volume when you want people to move, and lower the volume when you’ve achieved your goal. MGTaylor does this all the time, but they’re not the only ones. Open Space facilitators often use Tibetan prayer bells to signal transition. Allen Gunn (Gunner) will often start singing when he wants people to transition.    (MQS)

Working Like Sheep

An allegory, by Kirsten Jones:    (MOI)

I have a friend who hearkens from the heartland of America, and is thus more schooled than I in the cosmic truths to be found on a farm. We were discussing the relative dimness of various farm animals, and he mentioned that sheep were pretty much the stupidest animals around. I asked why, and he said that a lamb, when confronted with a meadow full of tall grass, will eat through the grass, leaving a 1-lamb-wide path behind them. When the lamb is full, however, it is faced with a horrible situation. Walls of grass surround it on the front and the sides. After looking left and right in a panic, the lamb will start to bleat piteously, hoping for someone to rescue it from its plight, eventually sitting down to wait until it’s hungry again so that it can extend the path further. I’m not sure how true the story is, but it makes for a compelling mental image.    (MOJ)

…    (MOK)

I frequently find myself in a position where I am trying to solve a difficult problem. The more I push, the more the answer eludes me, but I have this underlying fear that if I break away and come back to the problem with fresh eyes, I’ll lose the context I’ve worked so hard to achieve. The reality is that I’m just like the lamb. The answer I need will only be clear when I back up. The context I’ve built up is broken, which is why I’m not finding the answer.    (MOL)

When Kirsten first told me this story, she was specifically talking about programming, but I think the analogy holds for many endeavours.    (MOM)

Visualizing Wiki Life Cycles

On the first day of WikiSym in Denmark last August, I spotted Alex Schroeder before the workshop began and went over to say hello. Pleasantries naturally evolved into a discussion about Purple Numbers. (Yes, I’ve got problems.) Alex suggested that unique node identifiers were more trouble than they were worth, because in practice, nodes that you wanted to link to were static. Me being me, my response was, “Let’s look at the numbers.” Alex being Alex, he went off and did the measurements right away for Community Wiki, and he did some followup measurements based on further discussions after the conference.    (LSP)

As it turned out, the numbers didn’t tell us anything useful, but our discussions firmly implanted some ideas in my head about Wiki decay rates — the time it takes for information in a Wiki page to stop being useful.    (LSQ)

I had toyed with this concept before. A few years ago, I came up with the idea of changing the background color of a page to correspond to the age of the page. A stale page would be yellowed; an active page would be bright white. I had originally envisioned the color to be based on number of edits. However, I realized this past week that I was mixing up my metaphors. There have been a few studies indicating a strong correlation between frequent edits and content quality, so it makes sense to indicate edit frequencies ambiently. However, just because content has not been edited recently does not mean the information itself is stale. You need to account for how often the page is accessed as well.    (LSR)

(At the Wikithon last week, Kirsten Jones implemented the page coloring idea. She came up with a metric that combined edits and accesses, which she will hopefully document on the Wiki soon! It’s cool, and it should be easy to deploy and study. Ingy dot Net suggested that the page should become moldy, a suggestion I fully endorse.)    (LSS)

This past Sunday, I had brunch with the Socialtext Bloomington Boys. Naturally, pleasantries evolved into Matthew and me continuing along our Wiki Analytics track, this time with help from Shawn Devlin and Matt Liggett. We broke Wiki behavior into a number of different archetypes, then brainstormed ways to visually represent the behavior of each of these types. We came up with this:    (LST)    (LSU)

The x-axis represents time. The blue line is accesses; the green line is edits. Edits are normalized (edits per view) so that, under normal circumstances, the green line will always be below the blue (because users will usually access a page before editing it). The exception is when software is interacting with the Wiki more than people. The whole graph should consist of a representative time-slice in that Wiki’s lifespan.    (LSV)

The red line indicates the median “death” rate of Wiki pages. After much haggling, we decided that the way to measure page death was to determine the amount of time it takes for a page to reach some zero-level of accesses. We’ll need to look at actual data to see what the baseline should be and whether this is a useful measurement.    (LSW)

The red line helps distinguish between archetypes that may have the same access/edit ratio and curve. For example, on the upper left, you see idealized Wiki behavior. Number of edits are close to number of accesses, both of which are relatively constant across the entire Wiki over time. Because it’s a healthy Wiki, you’ve got a healthy page death rate.    (LSX)

On the upper right, you see a Wiki that is used for process support. A good example of this is a Wiki used to support a software development process. At the beginning of the process, people might be capturing user stories and requirements. Later in the process, they might be capturing bugs. Once a cycle is complete, those pages rapidly become stale as the team creates new pages to support a new cycle. The death line in this case is much shorter than it is for the idealized Wiki.    (LSY)

Again, one use of the Wiki isn’t better than the other. They’re both good in that they’re both augmenting human processes. The purpose of the visualization is to help identify the archetypes so that you can adjust your facilitation practices and tools to best support these behaviors.    (LSZ)

This is all theory at this point. We need to crunch on some real data. I’d love to see others take these ideas and run with them as well.    (LT0)

Kirsten Jones on Perlcast

Socialtext‘s Kirsten Jones talks about Socialtext Open and its REST API on this week’s Perlcast. It’s a good interview, and the last question reminded me of a funny exchange at WikiWednesday this past week:    (LH0)

Kirsten: PHP is for girls.    (LH1)

Evan Prodromou: Hey, Michele would object to that!    (LH2)

Kirsten: That’s because she’s a woman. PHP is for girls, Perl is for women.    (LH3)

WikiWednesday and Web Mondays

Two “days” coming up worth attending, for those of you in the Bay Area. Tomorrow is WikiWednesday at Socialtext in Palo Alto. Three good reasons to go:    (LGI)

Next Monday is the third WebMonday Silicon Valley, this time at Cooley Godward Kronish in Palo Alto. Sadly, I won’t be able to make this one, but I spoke at the last one, and I had an one excellent time.    (LGM)