Life at the fringes of Canadian federal politics: the experience of minor parties and their candidates during the 1993 general election
Bibliographic record
Abstract
This thesis marks the first attempt to systematically study Canadian minor parties. Minor parties, as distinct from third parties, are those that acquire less than 5 percent of the national vote (usually much less than one percent) and have never sent an MP to Ottawa. We know little about parties as a group except that their numbers have steadily proliferated over the last 20 years and that this growth shows no signs of abating. The goal of this paper is fill the knowledge gap surrounding minor parties and to assess the health of electoral democracy in Canada. Specifically, nine minor parties are studied through the experiences of their candidates during the 1993 federal election. The findings presented are based on data collected from government sources and on surveys and interviews administered to a sample of minor party candidates who ran in the greater Vancouver area. The dissemination of political beliefs not represented in mainstream politics was the dominant reason candidates gave for participating in elections. Winning is a long term ambition, but not expected in the short run for the majority of parties. Despite their modest aims, minor parties and candidates are unduly fettered in their ability to effectively compete in elections and communicate with the public. Minor party campaigns typically have scant political resources, including money, time and workers; electoral laws — concerning registration thresholds, broadcasting time allotments and campaign reimbursements — designed to promote fairness, disadvantage the system's weakest players; and subtle biases on the part of the press, debate organizers and potential donors close important channels of communication. Of these factors, money emerged as the most important, with media exposure — or the lack of it — a close second in terms of determining a party's competitiveness. The National Party, with superior resources, was often an exception to the above characterization, but ultimately, media neglect sealed its fate as a marginal party. Notwithstanding the great odds facing minor parties, winning is not impossible given the right alignment of factors. The Reform Party did it in 1993, providing other small parties with hope and an example to follow.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".