SYMECO - Focus Group Dataset on Sexual Corruption and Digital Public Services

This dataset contains anonymised, NVivo-derived coding matrices and analytical summaries from the SYMECO project (Marie Skłodowska-Curie Actions, Horizon Europe), focusing on sexual corruption and gendered interactions in public service delivery in Brazil. The data were produced through qualitative analysis of ten focus groups with 110 women, conducted in 2025, examining how sexual corruption, harassment, and gendered power asymmetries intersect with digital government platforms and technology-mediated services. The dataset does not include interview transcripts or direct quotations. Instead, it presents aggregated coding outputs and interpretive summaries structured across five analytic axes: (1) Digital Government – Access and Possibilities, (2) Gender Sereotypes in Service Delivery, (3) Sexual Corruption – Prevalence and Contexts, (4) Consequences, Avoidance and Withdrawal, and (5) Agency, Resistance and Protection. The Excel workbook comprises eight sheets:– Insights: axis-level interpretive summaries that synthesise the main patterns observed in each analytic axis.– Summaries Per Question: narrative summaries of each focus-group question, disaggregated by focus group (FG1–FG10).– By Code: percentage distributions of NVivo codes by focus group, distinguishing ten groups: FG1 High School Teachers, FG2 Middle School Teachers, FG3 Civic Movement, FG4 Women Association, FG5 Women Protection Group, FG6 Public University Students, FG7 Afro-Brazilian Women Group, FG8 Political Party Supporters, FG9 University Students and Professors, and FG10 Health Care Professionals.– By Age, By Education, By Socioeconomic: code frequencies expressed as percentages across age ranges, educational levels, and socioeconomic groupings, enabling analysis of how experiences and themes vary across these dimensions.– Word Freq and Visual Cues: word-frequency and word-cloud-based interpretive summaries, including top-level interpretations of tone, focus, and emotional content for each analytic axis. All data have been fully anonymised at the level of NVivo coding and aggregation, with no individual-level identifiers, no direct quotes, and no contextual details that would allow the identification of specific participants. The dataset is intended to support further research on sexual corruption, gendered public-service encounters, digital government, and institutional trust, as well as methodological work on qualitative coding and the analysis of marginalised women’s experiences of public service delivery.