The Power of Language: Refugee Settlement in Canada
Bibliographic record
Abstract
The purpose for participating in the project Interdisciplinary Dialogue on Global Refugee Crisis was to get a better understanding of why there is a Global Refugee Crisis, how welcoming the refugees might impact our intercultural practice as future Social Workers. Based on the educational forums and the online postings discussion we learned that language as part of the Refugees’ difficult journey continue to represent some barriers when landing in Canada. Our presentation is therefore exploring what we called the power of language – what power of language are we using to welcome the refugees? There is a universal language we all understand which is being gracious, kind and welcoming. However, there is also a language of fear, hatred and misunderstanding. The power of language focuses on how power imbalance in society is realized through language, specifically on the role of language in producing and maintaining oppressive and unequal relationships. We acknowledge in our poster the different languages already present in Canada and those that refugees are bringing to show that the language culture in the everyday life experiences for many of these refugees in Canada go beyond the bilingualism culture. From a micro level, we do believe that community connection can help newcomers establish social networks. Partnerships, promotions and planning sessions allow the community connections and bring diverse organizations together to welcome refugees in their local community, to overcome the hassles of refugees. And not but the last our acceptance that the languages that Refugees are bringing with them might break many of the barriers, we represented in our second poster, to make place for conviviality. Discipline: Social Work Faculty mentor: Dr. Valerie Ouedraogo
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.053 | 0.013 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".