The 2024 Belgian federal elections: a race to the right
Bibliographic record
Abstract
The Belgian federal elections held on 9 June 2024 marked a turning point in the country’s political history. As the first post-pandemic federal vote, one of the lowest turnouts was recorded since compulsory voting was introduced in 1893. An unprecedented share of voters supported radical parties, reflecting increased polarisation and a notable shift to the right. In Flanders, N-VA outperformed expectations, while VB’s anticipated breakthrough fell short. In French-speaking Belgium, the MR surged, while PS and Ecolo suffered historic defeats. These shifts led to the collapse of the incumbent ‘Vivaldi’ coalition and a fragmented parliamentary outcome. Ultimately, a new ‘Arizona’ coalition was formed, led by Bart De Wever, who became the first Belgian Prime Minister with a declared commitment to Flemish independence. This report provides an overview of the election campaign, the results that followed, voters’ underlying motivations, and the broader implications for federal governance in an increasingly divided political landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".