Bibliographic record
Abstract
Neighbourhood Shared Voting is a proposed electoral reform which involves transferring, to adjacent electoral districts, ballots which have not already contributed to electing a parliamentary representative in Canada. Simulations were done using the voting patterns of the five Canadian elections between 2004 and 2015. “Neighbourhood Shared Voting” is a primitive, simpler relative of STV (Single Transferable Vote) which uses the existing electoral districts and ballot. As with other PR systems, winners are determined one-by-one. It uses two steps, which are repeated until every seat is filled:1. The candidate with the most votes is declared a winner.2. Votes that were not for the winner are shared equally among the adjacent districts that do not yet have a declared winner (to candidates of the same parties), then step 1 is repeated. (Here “adjacent” is defined as connected by dry land or by an automobile bridge or tunnel.)Instructions:The NSV-YYYY-PP.xls files (NSV-year-province abbreviation.xls) are Excel spreadsheets with a macro which generates the election outcomes that would be obtained using the Canada federal election data from the 2004, 2006, 2008, 2011 and 2015 general elections if the Neighbourhood Shared Voting (NSV) system had been used to count the votes. Although the elections are federal, there is one file for each of the ten provinces for each election. (The results for the single seats in each of the three Canadian territories of Yukon, Northwest Territories and Nunavut are the same as under the existing electoral system, Single Member Plurality or 'First-Past-The-Post'.) The original voting data is from Elections Canada at www.elections.ca.The data input files require Microsoft Excel 2000 or a similar version of Excel. Double-clicking a file will open it in Excel, which will ask if the user wants to enable or disable macros; click "Enable macros". In the spreadsheet at O1 click the button "Click for Result" to generate the output for that province. Do not click the button a second time.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.756 | 0.717 |
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".