'Gender, ethnicity and political representation in the UK and Canada'
Bibliographic record
Abstract
Presentation embargoed pending a permission.\n\nConference presentation delivered at the European Conference for Politics and Gender / ECPG, (June, 2017), in Lausanne, Switzerland. \n\nThe extant literature on political representation demonstrates white men dominate political legislatures (Murray, 2014), whilst women and ethnic minorities are under-represented relative to their proportion of the population (Hughes, 2009). This pattern holds true for both the UK and Canada, although the UK House of Commons has moved closer to gender balance than Canada’s, while Canada’s House of Commons has moved closer to mirroring the country’s ethnic composition than has the UK. This paper longitudinally explores these representational differences. The research is underpinned by an intersectional analysis exploring the multiple ways in which gender and ethnicity interact at both the party level and in the national media. We argue the specific construction of ‘problematic’ political identities, influenced by the interaction between racialized and gendered stereotypes, makes it especially hard for ethnic minority politicians to become elected. Although the under-representation of ethnic minority women in the UK and Canada has attracted scholarly analysis, less attention has been paid to the ways in which gender and ethnicity interact to affect the representations of male and female ethnic minority politicians. Accordingly, this paper provides a multi-year comparative overview of ethnic minority descriptive representation in the UK and Canada and analysis of party responses to address ethnic minority under-representation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".