In each case, a gender-neutral search term was used based on the occupation description, and then the number of male and female images returned were manually counted, using the somewhat arbitrary (but consistently applied) metric of those results which appeared on screen with no scrolling. Cartoon and stylised images were included where gender was obvious; where more than one person was included in an image, all who were in focus and relevant were included. if their gender was not obvious. In a indonesia rcs data number of cases, it was necessary to select one person who matched the image description: thus, when searching for ‘carer’ it was generally the case that each image depicted both the giver and the receiver of care.
Table 1 shows the results for the top level of the SOC coding, giving both the numbers employed as reported in the census, and the gender split as determined by assessing the Google image results. The SOC labels here are rather broad, and inevitably in representing this as a single search term a significant degree of generalisation is required. A suitable search term for the final ‘elementary occupations’ group was not found. The results are quite interesting: in most cases the gender balance is in the right direction, whilst not being close enough to suggest that this an accurate mirror of society. The one category that is clearly wrong is ‘Administrative and secretarial occupations’ which is probably an artefact of the search term used.