Norwegian Public Library Language Cafés Facilitate Discourse Between Immigrants and Norwegian-Born Citizens
Bibliographic record
Abstract
A Review of: Johnston, J., & Audunson, R. (2017). Supporting immigrants’ political integration through discussion and debate in public libraries. Journal of Librarianship and Information Science, 29 May, 1-15. https://dx.doi.org/10.1177/0961000617709056 Abstract Objective – To investigate if conversation-based language cafés in Norway provide a platform for improving communication between immigrants and Norwegian-born citizens, potentially setting the stage for better participation by immigrants in civic dialogues. Design – Multi-site case study. Setting – Public libraries in Oslo, Moss, and Horten, Norway. Subjects – Language café participants (immigrants and Norwegian-born volunteers). Methods – Participant observation and questionnaires for immigrants (Norwegian, English, Somali, and Arabic language versions) and volunteers (Norwegian language only) who took part in café activities. Main Results – 64 immigrants (21 in Oslo, 30 in Moss, 13 in Horten) and 31 volunteers (7 in Oslo, 14 in Moss, 10 in Horton) completed questionnaires. Language cafés at all three sites led to informal, respectful discursive interaction between participants. Though each café had a unique set of participants and conversational topics, all cafés enabled immigrants to improve their Norwegian language skills while providing all participants with a place to meet new people, exchange information, and discuss political issues. Conclusion – Having attended the cafés and improved their knowledge of Norwegian language and culture, immigrants at all three sites were potentially better equipped for future participation in the Norwegian public sphere.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".