Perhaps you might object mathematically too. I think of a curve as fitting the test scores to a specific distribution as a corrective to testing error. It's not the fitting that's the issue, it's how justified the target distribution is. If you truncate it, you have to justify doing that somehow (all prior test results had that distribution?). Of course, if the sample distribution squashes it enough, you might end up clustered at 100% anyway, but...
Perhaps you might object mathematically too. I think of a curve as fitting the test scores to a specific distribution as a corrective to testing error. It's not the fitting that's the issue, it's how justified the target distribution is. If you truncate it, you have to justify doing that somehow (all prior test results had that distribution?). Of course, if the sample distribution squashes it enough, you might end up clustered at 100% anyway, but...