“Lagging Behind” : An Examination of Why Women Continue to be So Underrepresented in Canadian Federal Politics
Bibliographic record
Abstract
The purpose of this study was to explore the question of why women are still so underrepresented in Canadian federal politics and specifically within the Canadian House of Commons despite advances in representation in many other fields.To answer this question a study was conducted using qualitative data obtained from interviews with 17 female Members of the 42 nd Parliament of Canada between October 2018 to April 2019.Data collected through these interviews was analyzed qualitatively using a combination of content and discourse analysis to summarize, categorize, and investigate the verbal, written, and behavioural data that was obtained.Findings from this study mostly confirm the findings of previous research with a few key exceptions.New findings from this study include that a more nuanced relationship exists between female MP's and the media than previously thought; that most of the women who run for office at the federal level have very little or no knowledge of the nomination, candidate, and electoral process before they start; and that a toxic work place culture exists within the House of Commons and this negatively impacts the experience that female MP's have and is one of the reasons women are more likely to have shorter political terms and leave politics after shorter amounts of time when compared to their male counterparts.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".