Women's Equality & the Federal Election: Why Your Vote Counts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it