3 ms·
In part it's due to requirements of transparency. If some stranger is going to be evaluating you, a simple way to ensure that you're not being evaluated unfairl
by fluoridation 3y ago
In part it's due to requirements of transparency. If some stranger is going to be evaluating you, a simple way to ensure that you're not being evaluated unfairly is to have the evaluation criteria be very simple, almost to the point of being mechanistic. If you're part of a small enough community it's likely that your evaluator, their overseer, and your peers all know you, so it's very difficult for unfairness to creep in unnoticed. If your community is gigantic everyone starts to be strangers to everyone else, so people can't intuitively know whether someone is being treated unfairly or not. When everyone knows each other it's much more difficult to game the system, because people can tell when you've been lazying around.
- godelski 3y agoI don't disagree with this response. It's actually a good one. But I think we need to be really aware of the trade-offs here. My problem is that people use these metrics as if they are hard proof of competence. But the truth is you have to pick your noise: nepotism or lazy evaluation. Either way, you aren't getting rid of noise. I actually think scale is part of the problem here. In small communities there is more natural nuance and correction as there's high accountability. But with scale accountability often disappear as you disappear into the crowd. And you'll always be able to point at winners and losers to whichever noise you favor. The problem is we're not looking at things holistically and ensuring that our metrics are aligned with our desired outcomes. Instead we often pick a metric because it reasonably looks like it should align with the desired outcome (not proven, just intuited) and then just tell anyone that disagrees that it is obvious instead of acknowledging the downsides. Every metric has limitations. Ignoring them is the problem.