Gender assignment methodology.
Most candidates (~77%) had no gender recorded in the source data. Gender was predicted in three stages: (1) the gender_guesser library (an open-source name database) and the ONS historical baby names dataset (1904–2024), using birth year where available to account for names whose gender balance has shifted over time (e.g. “Ashley”, “Kim”); (2) Claude Sonnet 4.6 was used to classify names that remained unresolved after step 1.
63 candidates (<0.3%) remain unclassified — primarily names where gender could not be determined with confidence. Percentages are calculated only among candidates with a known gender.
Gender is treated as binary (male/female) for prediction purposes.
Incumbent status is determined by matching 2026 candidates against 2025 sitting councillors sourced from opencouncildata.co.uk. Matching uses council name normalisation, ward-level lookup, and full-name fuzzy comparison. Note: incumbents who chose not to re-stand are not counted.
Election type (full council vs partial) is sourced from the same dataset: councils electing all seats in 2026 are marked Full; those electing by thirds or halves are marked Partial.
Gender balance by council area
By party — parties with ≥30 candidates, sorted by total. Each bar = 100% of that party’s candidates/elected.
Candidates — gender breakdown
Elected — gender breakdown
ⓘ Click a bar to drill into that party — click again to clear
By region
Candidates — gender breakdown
Elected — gender breakdown
ⓘ Click a bar to filter by region — click again to clear
By council
Click a council row to drill down into ward-by-ward results. Click column headers to sort. Confidence shows what % of gender predictions are high-confidence — low values mean gender balance figures are less reliable.
| Council | Candidates | Female Cands | % Female | Confidence | Elected | Female Elected | % Female Elected | Inc. Retention | Avg Turnout | Election |
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