Noel Card (2011) discusses the merits/drawbacks for correcting for unreliability (and other artifacts, like artificial dichotomization) in his book. The text isn't the most sophisticated resource for meta-analytic methods--it's essentially a meta-analysis for beginners resource--but I do think it does a really good job presenting both sides for procedural decisions like the one you're thinking about (and many others).
In a nutshell, it's a decision that you need to make for yourself; some researchers support correcting for artifacts, whereas others do not for many of the reasons you offer above. For peer-review, it therefore is important for you to justify your decision--whether you correct for artifacts or not.
In my own opinion, I think there are likely occasions where artifact correction is useful, and other cases where it is less useful. For example, I am currently conducting a meta-analysis of correlations between two psychological variables, and often times they are just studied together because their connection seems very "intuitive". I have used artifact correction (for both unreliability and artificial dichotomization), and am finding that there is no significant meta-analytic correlation, despite the correction. So because I used artifact correction, I actually have a much stronger demonstration of there being no correlation, because the corrected correlations represent the strongest possible association (real-world-liness be damned) that the two variables could have. If I hadn't used artifact corrections, someone who supported the intuitive connection between the two variables might respond, "well, maybe unreliability of the measures is masking a true correlation!" And now I have direct evidence to the contrary.
Finally, you could always analyze your data both ways and present estimates from both analyses--with and without artifact correction. This is sometimes called a "sensitivity analysis" (Borenstein, Hedges, Higgins, & Rothstein, 2009). You can note then the differences, if any, between the two approaches to data analysis, and then justify one model over another, or illuminate circumstances where either model might be more useful.
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. West Sussex, UK: Wiley.
Card, N. A. (2011). Applied meta-analysis for social science research. New York, NY: Guilford Press.