A Critical Analysis of the Term Barbaric Cultural Practices through An Examination of the S-7 Zero Tolerance for Barbaric Cultural Practices Act and the Harper Conservatives’ Discourses
Bibliographic record
Abstract
In 2014, Canada’s Conservative government introduced the Zero Tolerance for Barbaric Cultural Practices Act (Bill S-7), with the aim to protect Canadian women and girls, especially Brown and Muslim women, against gendered-based violence that uses culture as an excuse. However, in the context of the war on terror and anti-Muslim discourses during the Harper years, the title of Bill S-7 appears contradictory to its intended goal. The Conservative government engaged in activities that appear to ignore the interests of women, especially minority women in Canada (Olwan 2013). Through a critical discourse analysis of Conservative Ministers’ Parliamentary hansards during the debates for Bill S-7, this paper finds that the conservatives’ mobilization of the term barbaric cultural practices has three functions: Bill S-7 sends a strong message that Canada will not tolerate barbaric cultural practices; the Government has a responsibility to pursue a humanitarian immigration system; Bill S-7 would protect all women and girls from gendered-based violence that uses culture as an excuse. Using Sara Farris’ femonationalism framework, this paper argues that Bill S-7’s three functions advance Conservatives’ nationalist and neoliberal interests through the enforcement of a type of “patriotic neoliberal citizenship,” which promotes Canadian values as militarism, close ties with the British Crown, and economic independence over multiculturalism, among others (Abu-Laban 2018).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.044 | 0.059 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".