Bibliographic record
Abstract
Violence against women is a global problem, which can be recognized in every society and culture. Both in Canada and Finland the research about violence against immigrant women has begun quite recently and therefore, there is still a lot we do not know about this phenomenon and thus a demand for research. Immigrant women face unique circumstances and are in a vulnerable position of being abused. They are not a homogeneous group, on the contrary, they have individual life experiences but they all have been born outside the country of immigration and are so part of the minority. Family violence is a gendered issue even though many researchers tend to use neutral terms and so to let the readers understand that both men and women can be equally violent. The reality is, nonetheless, unbalanced and it has been suggested that ”family violence” should be called ”male violence” since males are largely responsible for most of the domestic violence. This is the reason why in this thesis I focus only in the abuse against women, and especially against immigrant women.\n\nMy main question for this thesis is: what are the societal risk factors which make immigrant women vulnerable to partner violence. I believe that knowing an answer to this question might make it possible to help abused immigrant women in better way and gives information about what kind of changes should be done in the society so that immigrant women could free themselves from violence. To get more information about the topic I interviewed abused immigrant women and the workers who meet these women every day. I have used four major themes: racism, immigration status, social welfare and legal systems and collision of the cultures and built a semi-structured interview around them. The interviews were done in Canada but I write from a Finnish perspective so that the gained information could be used in Finland.\n\nThe literature review and interview data describe quite similar risk factors and reasons for abuse. A big difference between Canada and Finland is that the education and knowledge of those workers who work with immigrant women: in Canada there is a lot more experience and training about immigration issues. Finland has less immigrants and the awareness of the special needs of immigrants is quite new. There is a lack of culturally sensitive services and in my opinion, to improve the situation culturally sensitive work should be taught already during the social work university studies.\n\nAsiasanat:partner abuse, immigrant women, Canada, society, risk factors
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".