  The calibration of redshift distributions for photometric samples using
spectroscopic surveys is plagued by the difficulty in modelling the selection
functions of spectroscopic surveys. In this work, we analyse how these
selection functions impact redshift inference and quantify the induced biases
using local calibration tests in photometry space. The study is carried out
using simulations that mimic the radial selection function of a spectroscopic
survey and an accompanying mock catalog of a photometric galaxy survey catalog.
We use a self-organizing map to partition the photometry space and perform a
local $\chi^2$ test to study the probability calibration of redshift inferences
that use the spectroscopic data for calibration. The goal of this work is to
investigate the effect of uncorrected selection functions in the calibration
data on redshift prediction accuracy and critically discuss mitigation methods.
In particular we test culling-based bias correction techniques, that aim to
remove redshift calibration biases by identifying regions in photometry with
few spectroscopic calibration data, and propose avenues for future research. We
found that removing regions in color-magnitude space that are underpopulated
with spectroscopic calibration data does not remove all biases in redshift
inference induced by the selection function.
