Bridging the gap between ethnically/culturally diverse 'others': A contribution to the understanding of how women work together
Bibliographic record
Abstract
In this thesis, I explore the ways in which ethnically and culturally diverse women work together to rbedige their differences. Using a critical, feminist, reflexive and post-colonialist approach, I conducted eight in-person, semi-structured interviews with women who were staff and board members, volunteers, or participants in programmes offered by an organization serving immigrant women. Wommen were asked to name their cultural or ethnic identities, to share their views on multiculturalism, tolerance, and the “welcoming” of newcomers to Canada, the uniting and divisive issues they faced in their work, as well as appropriate roles for Canadian-born and immigrant women in the organization at which they work. According to my interviews with women and the organizational data, one of the main features of women’s work together has been their attempt to “fit in.” In the context of this particular organization, “fitting in” meant that women emphasized commonalities and swerved away from critical and political analyses, particularly around notions of colour, power and privilege. In addition, women within this organization adopted mainstream society’s “liberal’ view of multiculturalism, which celebrated women’s diversity, but did not make room for a deeper understanding of the differences between individuals from diverse cultural, ethnic, and racial groups. As such, ethnically and culturally diverse women tended to work together as “Canadians,” and swept aside their differences or challenges. Women’s responses to the questions regarding the “how” of their work together were impacted by their skin colour (visible minority vs. white) and experience with immigration (Canadian-born vs. immigrant to Canada). Colour was a salient predictor of women’s experiences, as visible minority women (regardless of their country of birth) were more forthcoming about their views on multiculturalism, tolerance, and the roles women should play within the organization. Generally, all participants were quite uncomfortable with critical language around colour, power and privilege, which was understandable given the organization’s downplaying of “political” issues, and our larger society’s avoidance of issues of power and privilege. To account for some of the “gaps” in communication between ethnically and culturally diverse women, I discuss the utility of an anti-oppressive framework and the abandonment of critical language (without a rejection of the underlying critical approach) in order to “build bridges” between diverse women working together in Canada.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.061 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".