Research Proposal: A Gender Gap or Gender Difference? Gender and Political Participation in Canada
Bibliographic record
Abstract
Studies find that men and women tend to do a similar amount of political participation, however, they tend to engage in different forms of participation (Bode, 2017, p. 598; Coffe & Bolzendahl, 2010, p. 330; Van Duyn et al., 2019, p.10; Pfanzelt & Spies, 2019, p. 45). Women tend to engage in more private and flexible forms, whereas men tend to participate in more direct and collective forms (Coffe & Bolzendahl, 2010, 330). However, there is variation when studies take into account the platform. Many studies mention political socialization or gendered socialization as a possible explanation for their findings in regards to women’s political participation trends, while others mention conflict avoidance or role models (Coffe & Bolzendahl, 2010, p. 330; Coffe & Bolzendahl, 2017, p. 149; Beauregard, 2016, p. 87; Bos et al., 2020, p. 477; Carreras, 2018, p. 40; Coffe & Bolzendahl, 2017, p. 149; Pfanzelt & Spies, 2019, p. 45; Caudillo, 2017, p. 128). In this proposal, I intend to discuss my literature review and how I will answer the following main research questions in my honours thesis: Is there a gender gap in overall political participation amongst Canadians? To what extent do views about politics being conflictual explain gendered differences in political participation in Canada? And, to what extent do female role models have an effect on Canadian women’s political participation? I will be using Jamovi programming to complete a quantitative study based on the secondary analysis of Canadian data from a 2021 Kantar administered study designed by Dr. Shelley Boulianne. Department: Sociology Faculty Mentor: Dr. Shelley Boulianne
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".