Bibliographic record
Abstract
Borda with None (BwN) adds the binding option of “None of these candidates,” N, to the Borda Count (BC), differing from a method of Dodgson in the scoring of ties. The method incorporates the benefits of approval voting, which allows a simple yes/no vote on each candidate and avoids the disadvantages of adding N as a binding outcome to plurality voting. We prove that BwN uniquely satisfies five rational properties which can be viewed as potential axioms that provide a theoretical basis for the method. It allows individual voters to express a personal veto over all or part of the slate which could, with sufficient numbers, become a veto by the electorate. The power of none, then, is offering a voice to otherwise disaffected voters. We introduce a metric similar to one used in the Bak–Sneppen Evolutionary model, candidate fitness, a number between 0 and 1, which measures a candidate’s ranking relative to N. We illustrate its evolution with a hypothetical example. We examine courses of action if N is ranked in the first place. Using BwN to accommodate partial voting is proposed. We explore the general applicability of BwN. Finally, we address the practical considerations for introducing BwN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".