Red Hat 'Community' and Corporate Puff Pieces
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Scrub gently: On data scrubbing in a community survey. - /home/jwf/
The point of this is that especially in larger communities, it is worth noting negative and harmful responses and not totally ignoring them. Communities that organize in more decentralized ways will always have supporters, users, and contributors from both the core and the periphery. The core project membership may not interact or engage often with the periphery often, so there can be a blind spot to parts of the project that identify with the community but are a few degrees removed from the inner ring of the project community.
Noting whether something is indicative of a larger pattern is important. If your community has a ton of jerks, you need to know that your community is full of jerks so that you don’t waste time persuading people otherwise, when the lived experience is very different.
In the original conversation with the CHAOSS Project team, this data scrubbing question emerged in the process of running the survey instead of after the data collection concluded. The survey later closed and our data manager confirmed that the flagged response from earlier was the only one of its kind. As a group, we then felt more confident in discarding that one outlier as an anomaly since the survey was open to the general public.
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What do you do with community metrics?
In my previous article, I provided an overview of possible community health metrics. I look at what you can do with those metrics in this article. You'll see several examples from different communities, some of which you may be familiar with.
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I'll start with the "new contributors and contributions" metric, which measures developers joining and leaving a community. I can measure this by seeing which developers made a commit during a specific period. Someone who shows up for the first time joined. Someone who hasn't contributed for a while has probably left.
It is natural for developers to leave a project. Maybe they change jobs, have a change in priorities, or have personal reasons for reducing their open source engagement. It is important for the health of an open source project to attract new developers to continue the work.
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Machine learning: 4 adoption challenges and how to beat them
In the first quarter of 2022, global funding to artificial intelligence (AI) startups reached $15.1 billion, according to CB Insights’ State of AI report. However, machine learning (ML) algorithms can lead to counterproductive results when deployed without reason.
Here are four common challenges that companies implementing ML-based systems may encounter, along with some expert tips to maximize the impact of algorithms while avoiding missteps.
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Digital transformation: Don’t be a follower
There’s a big difference between taking on a digital transformation initiative and making it succeed. Unfortunately, many companies don’t see the fruits of their labor. According to a Boston Consulting Group study, 70 percent of digital transformations fall short of their objectives.
When that happens, there are grave consequences related to time, money, and organizational effort. That’s in addition to falling behind competitors in innovation, customer engagement, and technology, among other areas.