Churn Without Fragmentation: How a Party-Label Bug Reversed My Headline Finding

2026-05-01 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

Between 2018 and 2022, English urban councils experienced a significant increase in electoral volatility, with the median volatility score rising from 12.0 to 22.5. However, this heightened churn did not lead to a fragmentation of the party system; the effective number of parties increased in only 18 of 67 comparable authorities, and the median change in the fragmentation index remained slightly negative at -0.31. This corrected finding emerged after fixing a critical categorical data bug that initially misrepresented fragmentation as having risen in 66 of 67 councils. The analysis, based on the DCLEAPIL v1.0 dataset, attributes the volatility to a Conservative vote share collapse (median -8.3 percentage points) largely absorbed by Labour (median +8.5 percentage points), with Liberal Democrat and Green surges concentrated geographically rather than nationally.

Key takeaway

For data scientists building analytical pipelines with categorical data, you must prioritize category normalisation early in your workflow. Failing to explicitly define and normalize analytical categories before aggregation can lead to fundamentally flawed metrics and conclusions, as demonstrated by the misrepresentation of electoral fragmentation. Always stress-test your categorical filters and thresholds, and be prepared to publish null findings to ensure data integrity.

Key insights

Categorical data errors can propagate through pipelines, distorting metrics and leading to incorrect analytical conclusions.

Principles

• Normalize analytical categories before metric aggregation.
• Validate headline metrics against supporting metrics.
• Stress-test categorical filters and thresholds.

Method

The analysis pipeline ingests ward-level election results, normalizes party families, aggregates vote shares, computes fragmentation and volatility metrics, and exports structured CSVs for visualization.

In practice

• Separate display labels from metric definitions.
• Inspect edge cases admitted by categorical filters.
• Publish null findings to prevent false narratives.

Topics

• Party System Volatility
• Electoral Fragmentation
• Data Categorization Error
• Party Family Normalization
• Laakso-Taagepera Index

Code references

• Wisabi-Analytics/civic-lens

Best for: Data Scientist, Data Engineer, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.