When to Coalesce: Early Versus Late Coalition Announcement in an Experimental Democracy
Bibliographic record
Abstract
In multi-party democracies, several parties usually have to join together in coalition to form government. Many aspects of that process have been fairly fully investigated, others less so. Among the latter is the timing of the formation and announcement of coalitions. While the dominant popular image may be one of parties meeting together after the election to hammer out a coalition agreement, pre-election coalitions of one sort or another are actually quite common. In almost half of the elections in OECD (Organization for Economic Co-operation and Development) countries since the Second World War, at least one pair of parties had pre-announced their intention to join together in government. A quarter of governments formed were based wholly (and another quarter in part) on pre-election agreements. To date, such studies as there have been of pre-election coalitions have concentrated primarily on system-level explanations – features of the electoral system (majoritarian or proportional, and so on) that make such arrangements more or less likely.3 Here we shall instead look more at the agent-level logic of ‘early’ (pre-election) versus ‘late’ (post-election) coalition formation, from the point of view of voters and parties. hypotheses concerning coalition timing In the tradition of Downs and Riker and their coalition-theorist progeny, we shall assume that voters are interested primarily in getting policies adopted which are close to their ‘ideal points’ in policy space, and that parties are interested primarily in winning office to implement policies as close as possible to their ‘ideal points’ in policy space. That leads parties to strive for ‘minimal connected winning coalitions’: ‘connected’ in the sense that they link parties adjacent in policy space; ‘minimal’ in the sense that they involve the party's sharing power with the fewest parties backed by fewest voters that it can and still win.
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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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".