A Dynamic Model of Political Party Equilibrium: The Evolution of ENP in Canada, 1870-2015
Bibliographic record
Abstract
The effective number of political parties (ENP) in a first-past-the-post single member (SMP) electoral system is analyzed as a dynamic process whereby the tournament nature of the election contest induces excessive entry and sunk entry costs promote persistence even as Duverger-Demsetz type political competition works to winnow unsuccessful minor candidates and parties. The result is a fringe of parties circulating in long run equilibrium. The factors hypothesized to affect the entry and exit of candidates and parties are analyzed first using an auto-regressive distributed lag (ARDL) model that allows for the separation of an evolving equilibrium time path from short run variations in response to transitory changes in conditioning variables and the process of convergence back to the long run equilibrium. The possibility that the short run adjustment process is asymmetric either for parties or candidates is tested adopting panel estimation techniques. The results are consistent with an observed time path for parties that incorporates slower adjustment to positive as opposed to negative shocks. Variations in the size and trend of both the long and short run are then examined for ENP’s ability to predict changes in the competitiveness of the Canadian federal electoral system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".