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On the contrary, processes of gender recognition did not appear significant in our quantitative analysis, suggesting that caste and gender aspects of recognition justice operate at different spatial scales (section 3.1.3), as VDC-level spatial analysis appeared useful for the caste variable, but less so for the gender variable.
The caste variable was coded as scheduled caste, scheduled tribe, "other backward class", general caste, and missing or no caste.
We operationalized the caste variable as having the following categories: Scheduled caste, Scheduled tribe, "Other" caste and "missing/no" caste.
The caste variable has been classed in SC/ST, OBC and others.
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The caste-ethnicity variable reflects the important socio-cultural structure of Nepali society [ 18- 20].
Nine differed significantly by the variables caste and/or development.
Attention is paid particularly to the caste and gender variables, due to the focus on such inequalities by national policies and development programmes.
Finally, we examined interactions between socioeconomic variables (caste, education and wealth) and diabetes by residential location.
We modeled methylation levels for each genic feature as a function of two categorical variables: "caste" and "CpG position" using generalized linear models (GLMs) of the binomial family, implemented in the R statistical computing environment (R Development Core Team 2011).
The contribution of each variable to caste discrimination was examined using stepwise discriminant function analysis [ 34].
The caste and ethnic group variables were merged into six categories.
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