Bibliographic record
Abstract
Elections are an expensive exercise of democracy, often requiring vast amounts of money to fund candidates, parties, and officials, etc., to make the system work. However, this very same process can affect voter behaviour and eventual election results. Using the 2011 Canadian Federal Election, I compiled the financial data of all candidates from the three major federal parties (Liberal, Conservative, and New Democratic) from the Elections Canada database and investigated who gave money (how many donors, and how much was the average donation), how much in loans the candidates took out, and how much they spent. I then assessed the data’s correlation to winning, as well as external influences such as media, geography, Canadian federal laws on election spending, and other intangible qualities such as a candidates’ likability. Evidently there were a few problems with the methodology, among the presence of “outliers” (candidates who spend excessively higher or lower than average), which may skew the results. There were also a few cases where candidates did not disclose their finances (such as Nancy Charest). In the end, one can conclude that money donated and spent makes a slight difference in the election results. However, there are other factors which affect a candidate’s chances outside of money: 1) Perception of candidate/party/leadership; 2) Candidates’ background (race, gender, locale); 3) Media coverage and “spin”; 4) Incumbency; 5) Geographical location of the riding; 6) Demographic makeup of the riding.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".