Hung Parliaments and the Challenges for Westminster and Whitehall: How to make minority and multi-party governance work
Bibliographic record
Abstract
The return of a hung parliament at the 2010 general election is a serious possibility. But due to Westminster's limited recent experience of parliaments under 'no overall control' there is little institutional memory in Whitehall or Westminster, and even less public understanding, of what the implications would be. This article sets out to analyse the principal challenges that would be faced by government, opposition, parliament and the media in the event of a hung parliament. Drawing on experience from Canada, New Zealand and Scotland, we discuss the difficulties that may arise during the immediate government formation process and in the course of making minority or multiparty governance work on an ongoing basis. We conclude that a hung parliament need not undermine political stability or effective governance, but that all actors would need to adapt their behaviour and should therefore prepare carefully for this eventuality.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".