Questioning Prime Ministers: Procedures, Practices and Functions in Parliamentary Democracies
Bibliographic record
Abstract
This thesis investigates parliamentary oral questioning mechanisms that involve prime ministers in \nparliamentary democracies. Considering the fact that prime ministers are powerful and visible actors in \nparliamentary democracies, and that accountability is a key component of democratic politics, it maps \nthe mechanisms through which parliamentarians may question prime ministers in different countries, \nand explores the extent to which these mechanisms contribute to accountability, and the extent to which \nthey perform other functions. \nThe first research component is a survey of procedural rules regarding mechanisms through which \nparliamentarians may question prime ministers in 31 parliamentary democracies. It draws on an indepth examination of parliamentary rules of procedure, followed by a consultation with practitioners and \nofficials in each country to uncover aspects of convention and practice. Subsequently, questioning \nmechanisms are classified based on dimensions such as their collective or individualised nature, the \nextent to which procedures allow more open or closed participation, as well as the degree of questioning \nexposure to which prime ministers are subjected. It then discusses how these dimensions might affect \nthe practice of questioning. \nDrawing on these classifications, the second research component investigates the practice of questioning \nprime ministers in four countries: two using collective questioning mechanisms, where prime ministers \nare questioned together with ministers (Question Period in Canada, Question Time in Australia); and two \nusing individualised mechanisms, where prime ministers are questioned alone (Prime Minister’s \nQuestions in the UK, Oral Questions to the Taoiseach in Ireland). This second component relies on \nquantitative and qualitative content analysis of transcripts of parliamentary debates for each case study \ncountry. Departing from the assumption that parliamentary questioning mechanisms are designed to \nfacilitate accountability, it investigates the degree to which they do so, and the degree to which they \nperform other functions, such as facilitating the expression of conflict, support, or territorial \nrepresentation.
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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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".