When the State is Complicit in Gender-Based Violence against Women: Exploring Experiences of Housing Violence among Punjabi Women International Students in the Greater Toronto Area (GTA)
Bibliographic record
Abstract
Abstract: This article situates housing violence experienced by Punjabi women international students (ISs) in Toronto, Canada, as an aspect of structural gender-based violence (GBV) in which the Canadian state is complicit through its policies, laws, actions, and inactions. This works in tandem with patriarchal cultural practices by local Indian/Punjabi homeowners who rent out rooms to ISs and/or employ them. More specifically, state policies and practices allow the diasporic patriarchal cultural practices to be reproduced in ordinary interactions between ISs and homeowners. Both the state and patriarchal diasporic cultural practices contribute to structural violence against women ISs. The notion of neo-racism is utilized to understand the hostility faced by Punjabi ISs within the Punjabi diaspora. The authors draw on intersectional, anti-racist, feminist migration studies and interviews with 12 Punjabi women ISs and key informants conducted during the Covid-19 pandemic in the Greater Toronto Area (GTA). Résumé: Cet article établit la violence face au logement subie par les étudiantes internationales (EI) punjabi à Toronto, au Canada, comme un aspect de la violence sexiste structurelle (VBG) dont l'État canadien est complice par ses politiques, ses lois, ses actions et son inaction. Cela va de pair avec les pratiques culturelles patriarcales des propriétaires indiens/punjabi locaux qui louent des chambres aux EI et/ou les emploient. Plus précisément, les politiques et les pratiques de l'État permettent aux pratiques culturelles patriarcales de la diaspora d'être reproduites dans les interactions ordinaires entre les EI et les propriétaires. L'État et les pratiques culturelles patriarcales diasporiques contribuent tous deux à la violence structurelle contre les étudiantes internationales. La notion de néo-racisme est utilisée pour comprendre l'hostilité à laquelle sont confrontées les EI punjabi au sein de la diaspora punjabi. Les auteurs s'appuient sur des études intersectionnelles, antiracistes et féministes de la migration, ainsi que sur des entretiens avec 12 femmes punjabi EI et des informateurs clés menés pendant la pandémie de Covid-19 dans la région du grand Toronto (RGT).
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".