Narratives of settlement : immigration, change and identity among new immigrants to Winnipeg
Bibliographic record
Abstract
My objective in this research was to document the settlement narratives of new immigrants who recently immigrated to Winnipeg, Manitoba. Specific focus was placed on how individuals who share common settlement experiences had distinct interpretations, explanations and definitions of these similar events. Fifteen participants contributed to this research by doing semi-structured interviews. I posited that the participants would have cohesive stories about their settlement experiences, each with a beginning, a middle, and an end. This hypothesis was based on the theory that people present their lives to themselves and others in the form of narratives, either verbally or internally, to create coherence, consciously or otherwise, out of sometimes contradictory or confusing events and feelings. All narratives were informed in their own way by intersubjective experience and knowledge. Many of the participants had similar notions of migration, and many also had shared or partially shared norms and social understandings pertaining to gender, education, socio-economic status, and English as a second language. However, each narrative also reflected the distinctiveness of each participant. The participants' narratives were shaped by their particular experiences of culture and identity, transnational connections across borders, and relationships with both co-ethnic groups and others. Several key issues emerged from this research. The participants described many experiences which demanded different kinds of adaptation and change, and each pointed to different strategies with which they responded. The participants had a shared knowledge of how immigration and settlement commonly occur, and by their accounts their own experiences were distinct and often deviated from this norm. The definitions and meaning that each participant applied to his or her settlement process were highly personalized, reflecting the circumstances of his or her settlement and his or her individual identity. By presenting and discussing new immigrants' narratives of settlement, this research represents the participants' untold stories of the moving home which, when told, are verbalizations of the process of movement, adaptation, change and growth into new cultural surroundings and a modified personal identity. This research allowed for a greater understanding of the individual experience of settlement, and for the realization that there are multiple, diverse kinds of immigration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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 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".