ScholarOne - Paired Voting System(PVS) WINS,TIES,FORS,FIRSTS Sole Author: Denis J. Alarie. P.Eng. Contact Information: 66 Woodbine Ave Kitchener, Ont, Canada N2R1V1 djalarie@gmail.com
Bibliographic record
Abstract
This manuscript elucidates the Paired Voting System (PVS), a preferential voting system that builds upon the principles outlined by (Nanson 1882, 206; Wikipedia 2023). PVS orchestrates a majority-driven ordering of options, meticulously deriving a hierarchy from the collective preferences of voters. The system operationalizes four pivotal metrics—’WINS,’ ‘TIES,’ ‘FORS,’ and ‘FIRSTS’ in that order—all of which determined from Voter Ranked Preferences (RNKs). PVS transcends the individual ordinal selections of RNKs, synthesizing these inputs to construct unambiguous, majority-endorsed rankings. This places PVS as a significant alternative within the spectrum of voting methodologies. The manuscript provides an in-depth analysis of PVS’s functional dynamics, evaluates its capacity for decisive decision-making, and examines its potential to serve as a foundational structure for the Paired Proportional Weighted Voting System (PPWVS). The latter is an advanced implementation of PVS that amalgamates the weights of first and second place rankings from multiple districts, thereby shaping legislative decision-making with a more representative electoral calculus.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".