A RACIALIZED SETTLER WOMAN'S TRANSFORMATIVE JOURNEY IN CANADA: BUILDING RELATIONAL ACCOUNTABILITIES
Bibliographic record
Abstract
This dissertation tells the story of my racialized settler woman’s transformative journey toward reconciliation and mutual empowerment through community in Canada. The dissertation discusses how Indigenous Land-based learning became healing and empowering for me as a newly arrived settler woman of a colour, learning about my positioning on the stolen Indigenous Lands of treaty six territory. It recounts the journey of migrating from one colonial Land to another, building a family and new community networks, and learning about Indigenous histories, cultures, Land-based learning, and about diverse newcomer settler communities in Saskatoon, Canada. The dissertation discusses how collaborative learning has supported taking responsibility for understanding the meaning of Land in solidarity with Indigenous and newcomer communities, through involvement in a community garden project, community radio show, and various cultural community activities. Using decolonial feminist relational autoethnography as my research methodology, this dissertation discusses my quest to challenge everyday racisms and colonial practices ingrained in the daily lives of newcomer Canadians. Following 12 years of community activities in Treaty 6 and 7 territories, this research emphasizes a key lesson from this life journey: the need to be responsible for understanding the Indigenous meaning of Land in order to create belongingness with the Land and its original peoples, while resisting the assimilationist forces impacting Indigenous and newcomer communities through their unique histories, despite the orchestrated biases operating through colonialist structures. The author concludes with the hope that the analysis of decolonial, collaborative learning stories and connections with the Land may help other non-Indigenous communities build meaningful relationships with the Land and Indigenous communities.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".