  Strong gravitational lenses are a rare and instructive type of astronomical
object. Identification has long relied on serendipity, but different strategies
-- such as mixed spectroscopy of multiple galaxies along the line of sight,
machine learning algorithms, and citizen science -- have been employed to
identify these objects as new imaging surveys become available.
  We report on the comparison between spectroscopic, machine learning, and
citizen science identification of galaxy-galaxy lens candidates from
independently constructed lens catalogs in the common survey area of the
equatorial fields of the GAMA survey. In these, we have the opportunity to
compare high-completeness spectroscopic identifications against high-fidelity
imaging from the Kilo Degree Survey (KiDS) used for both machine learning and
citizen science lens searches.
  We find that the three methods -- spectroscopy, machine learning, and citizen
science -- identify 47, 47, and 13 candidates respectively in the 180 square
degrees surveyed. These identifications barely overlap, with only two
identified by both citizen science and machine learning. We have traced this
discrepancy to inherent differences in the selection functions of each of the
three methods, either within their parent samples (i.e. citizen science focuses
on low-redshift) or inherent to the method (i.e. machine learning is limited by
its training sample and prefers well-separated features, while spectroscopy
requires sufficient flux from lensed features to lie within the fiber). These
differences manifest as separate samples in estimated Einstein radius, lens
stellar mass, and lens redshift. The combined sample implies a lens candidate
sky-density $\sim0.59$ deg$^{-2}$ and can inform the construction of a training
set spanning a wider mass-redshift space.
