Bibliographic record
Abstract
The issue of the power of language as Integration Barriers is what my poster is illustrating. My work is inspired from the three-educational forum and the online postings discussion during the Interdisciplinary Dialogue on Global Refugee Crisis which were related to my bachelor social work course on Intercultural Practice in Social Work. How do we depart that umbrella that promotes English Language as the superior language and one everyone should speak, into a much wider box that embraces diversity, differences, and equality? According to Statistics Canada, in 2011 about 111 different were reported being spoken at home, a number that may have increased as of today. Amongst all the recognized languages, there isn't one I recognize from the few African I have heard spoken here in Canada. What I found most interesting in this research is that all the indigenous are included in the other spoken at home category rather than the primary languages category. If this was about speaking the language of the owners of the land, shouldn't we be stressing about the importance of learning Cree instead of English? Since English language is being imposed on all refugees and newcomers, should we then safely assume that we are just experiencing another form of colonization? Asking people to suppress all 111+ that form a big part of their identity when they step out of their homes, fuels racism and segregation, promotes isolation, prejudice, identity loss, and suppresses the integration process. Trying to meet them somewhere along the way, showing interest in their cultural ways, languages, and individual views, says you care and value their individual identity. Discipline: Social Work Faculty mentor: Dr. Valerie Ouedraogo
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.049 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".