Elections, Cleavages and Voting Behaviour: From Stability to Volatility
Bibliographic record
Abstract
Introduction In the pillarized Dutch society, election results were once very predictable. In the 1950s, a gain of two or three seats was celebrated as a resounding victory. Many people voted their entire lives for the party that represented the pillar they belonged to (see also Chapter 5). The shares of seats in parliament were therefore highly stable. The three major Christian parties, the Catholic People's Party, the Christian Historical Union and the Anti-Revolutionary Party, which later merged into Christian Democratic Appeal (CDA), the labour Party (PvdA) and the liberal VVD (People's Party for Freedom and Democracy) received between them more than 80 percent of the votes. From the middle of the 1960s, this began to change. Electoral swings were increasing, and over the last two decades we have seen more and more fragmentation of in the Dutch party-political landscape. After the 2017 elections, at least four parties were needed to form a coalition that had a majority in the Lower House of Parliament. The results of those elections highlighted major changes, in particular because of the losses sustained by the two ruling parties, the VVD (8 seats lost) and the PvdA (that lost 29 seats). This was not the first election in which there were important electoral shifts. In 1994, the CDA lost 20 seats and D66 won 12. In 2002, the List Pim Fortuyn (LPF), the new party of the assassinated politician Pim Fortuyn, entered the Lower House with 26 seats. The PvdA lost 22 seats; the VVD, 14. It is no longer that surprising if a large gain for a party is followed at the next election by a big loss. While the fluctuations in electoral results in the Netherlands are quite considerable, this rise in instability is an international phenomenon. To an important degree, the higher fluctuations go hand in hand with the emergence of new parties, especially environmental and anti-immigration parties, and the declining support for Christian Democratic and social-democratic parties. Major electoral changes are taking place not only in many Western European countries, but elsewhere as well, like in Australia and Canada. In this chapter, we will look first of all at the function of elections in a democratic system.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".