Reimagining Canadian Citizenship in Newcomer Serving Sector: From White Civility to Land Stewardship
Bibliographic record
Abstract
Drawing on reflections from a community action research project on Indigenous-refugee relations in the context of settler Canada, this paper critiques the current script of Canadian citizenship that shapes the newcomer-serving sector. We call for a discursive shift of Canadian citizenship, from the one that is built on white civility, to the one that is centred on land stewardship. We highlight a personal story of migration, immigranthood, and work with newcomer communities and argue that when the script of citizenship operates within racial and colonial politics, it invisiblizes racism, settler colonialism, and global geopolitics of migration and evokes a sense of disconnection and placelessness. We draw on the teachings of the Hodinohso:ni Thanksgiving Address from Cayuga Elder Gae Ho Hwako Norma Jacobs and theorization of landed citizenship by Annishinaabe legal scholar John Borrows and discuss the transformational potentials of land-centred citizenship for the newcomer-serving sector, specifically regarding Citizenship Test preparation, Official Language Education, and Community Connection Program. We suggest that when newcomers are equipped with land-centred knowledge and experiences in their integration process, they are more likely to foster a sense of belongingness that honours Indigenous history, presence, and sovereignty, which ultimately contributes to meaningful truth and reconciliation work.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.056 | 0.041 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".