Resilient Cartographies: A Systems Analysis of Resilience Among Indian Women Immigrants in Canada
Bibliographic record
Abstract
Most of Canada’s population growth is driven by immigration. In 2020, over 80% of Canada’s population growth came from immigration, with more than half of this number being economic immigrants admitted primarily to meet labour-market shortages. Among these, the incoming numbers from India have been the highest. This number is likely to grow in the coming years as the government calibrates immigration policies to meet its economic (labour-market), political (nation-building), and social (demographic) objectives. Moreover, from an individual perspective, it is clear that many are choosing Canada as their choice of immigration destination over other countries. \n \nGiven this context, the study looks at individual immigration journeys of women and acts of resilience within them through a human-centered systems focus. Immigration is a journey of change and uncertainty. Immigrant women from India adapt to vast amounts of changes, losses, and unpredictabilities throughout their journeys across both internal and external realms. The research examines how women immigrants from India adapt to these ambivalences and remain resilient. \n \nThe report traces an individual journey through stages including planning, moving, arriving, settling, integrating, and thriving and examines the cycles of change and resilience while unpacking the invisible systemic factors that influence each stage. Additionally, the research contests the policy gaze, which adopts a simplistic and prototypical view of the immigration journey by uncovering five immigration patterns or pathways that frame individual journeys. These include linear, serial, circular, onward, and return migration. Individuals who move in these patterns possess unique mental models and behaviours and relate differently to their immigration experience. \n \nThis understanding of fragmented and nonlinear journeys presents novel individual and systemic intervention opportunities. The study concludes with sixteen thought-starters for innovation. The purpose is to engage multi-stakeholder dialogue and co-creation to design an ecosystem of support that promotes immigrant communities’ capacities for resilience.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".