Canada's 2021 Federal Election: A Study on Voting Intentions Throughout the Election
Bibliographic record
Abstract
The 2021 Federal Election in Canada was a fascinating time for those in Canada. throughout the entirety of the election, media and news outlets found it difficult to predict a winner until the very end of election night on September 20 , 2021. This difficulty stems from constant shifts in both Justin Trudeau's and Erin O'Toole's voter favourability and preference. The paper, through the use of extended literature reviews and analysis, looks to provide some reasoning behind why voting intentions shifted immensely throughout the election. This paper focuses on three key factors starting with political factors. This area studied both the Liberal and Conservative platforms in relation to Canadians' top five issues for the 2021 election. After these two areas were analyzed, the focus shifted to issue ownership and whom Canadians believed had the best policies concerning their leading issues. The second factor studied was societal factors. This area selected five major events occurring in Canada before and during the 2021 Federal Election. These five areas were then studied to see how they may have affected voters' intentions. The third and final factor studied was communicative factors. This area was pinned down to one specific event, the 2021 Federal Leaders Debate on September 9 . th Following the conclusion of this event, Erin O'Toole immediately witnessed a fall in the polls, moving him out of the lead just ten days before election day. Both the non-verbal and verbal communication was studied. Through these three factors, this paper was able to produce one final prediction about why voting intentions shifted so drastically throughout the 2021 Federal Election.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".