@lizellelubbe based on what you’ve said - it seems then the defocus is a non-issue? And therefore for a future large dataset, a defocus range of -0.5 to -1.5 (or maybe 2.0-2.5 max) would work. High defocus range is still worse, just not as bad as would have been when CTF envelopes were worse. Unless you collected the same number of images < 1.5 um and ended up with wildly fewer particles. I assume you are using a Krios at ~64kx nominal mag based on the pixel size.
You should try whatever mask you think might help and judge the results based on map quality in the regions you care about most! In other words, do whatever makes it easiest to build your model. It is very hard to predict what will work and what won’t, but fortunately cryoSPARC is pretty fast so you can explore the parameter space and different masks, etc. without worrying about whether it’s “worth it” to try.
Also, run the same refinement a couple of times and compare the results - you will see they are not the same (even if you use the same random seed). Any conclusion about different refinement parameters should only be drawn in the context of the expected variation from repeating refinement with the same parameters. With regard to the spikiness of the 280px map, that is likely just appearances, try vop resample #N spacing 0.53 to see if it looks smoother. Coot actually always resamples the map, the default factor is 1.8 I think.
CTF aliasing would in theory just be another b-factor that attenuates the high resolution signal, so if it (or delocalization) were significant/limiting then the bigger box size would have given a better result. Also, about the FSC dip, that is likely an intrinsic property of the shape of your protein (you would have to dig down and look at its radial power spectrum, and also consider the orientation distribution, to be sure). Finally, the FSC curves are generally less smooth with the bigger boxes because the spectral resolution is higher (same Nyquist frequency, more samples).
PS I understand you may be limited by particle count, but the best way to improve resolution at this point is very likely 3D classification in Relion or cisTEM using some combination of masking and alignment settings (no alignment, or certain fineness or resolution limit). You can come back into cryoSPARC to clean things up with NU afterwards. Another thing that might help the larger boxes is one more round of 2D classification, to throw away particles which have too much background in the big box.