"But Where Are You From, Originally?" Immigrant Women and Integration in the\n Maritimes
Bibliographic record
Abstract
This paper examines the process of integration as a complex, multi-faceted and gendered phenomenon that is substantially different for men than for women.We explore how volunteer community involvement provides immigrant women with a safe opportunity to explore, train and get acquainted with Canadian society, as this involvement serves as a means and a vehicle for immigrant women to break the isolation, make friends, enact citizenship through their activism for social change and even, sometimes, find employment.We also discuss reduced funding and its profoundly negative impact on the integration process of immigrant women into Canadian society. RESUMECet article examine le processus d'integration en tant que phenomene complexe, a plusieurs facettes et faisant I'equilibre entre les sexes qui est considerablement different pour les hommes et pour les femmes.Nous explorons comment ['engagement volontaire de la communaute fournit aux immigrantes une occasion oil elles peuvent en toute securite explorer, apprendre, et se familiariser avec la societe canadienne, etant donne que cet engagement sert de moyen et de vehicule aux immigrantes de briser 1'isolation, de se faire des amis, de promulguer la citoyennete par leur activisme pour le changement social et meme parfois pour se trouver un emploi.Nous discutons aussi des compressions budgetaires et de leur impact profondement negatif dans le processus d'integration des immigrantes dans la societe canadienne.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".