Women's Equality & the Federal Election: Why Your Vote Counts
Bibliographic record
Abstract
Women fought hard and even died to gain the right to vote alongside men. It can be challenging to sift through the information from all the political parties during an election. How important is voting anyways? What impact does it have? \n\nWomen’s Equality & the Federal Election: Why Your Vote Counts was a non-partisan public education event promoting voting among women and awareness of issues impacting women in the federal election. This event brought together leading women’s right experts, economists, community leaders, and candidates from all federal political parties for an informative dialogue on issues impacting women in Canada.\n\nTopics for discussion included childcare, wage equity, economic inequality, housing, discrimination, the importance of voting among women, and women’s political leadership and representation in Canada’s federal government. A woman candidate from the Liberal, NDP and Green parties explained their party election platforms on key issues impacting women and discuss how their party will address gender inequality. \nPanel discussion with Shelagh Day, Iglika Ivanova and Cherry Smiley, plus information from Grace Lore, Equal Voice, on women in politics, was moderated by Erica Johnson. The federal party representatives included Constance Barnes, Lisa Barrett  and Dr. Hedy Fry.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".