The Newcomer’s Guide to Edmonton and Community Translation: Materially and Culturally Situated Practices
Bibliographic record
Abstract
In line with the current turn towards the study of material culture in Translation Studies, this paper explores community translation in Edmonton through the case study of the Newcomer’s Guide to Edmonton (NGE). A 63-page handbook of essential information for new residents published by the City of Edmonton (2016), the NGE was translated into 7 languages in a project that employed community translators. This research examines community translation as both a materially and a culturally situated practice. We discuss how the materialities of communication and translation (Littau, 2016) can be addressed through this case study on community translation (Taibi and Ozolins, 2016). We also look at the process of community translation, specifically, the material conditions under which community translators work, often as volunteers with limited training who serve newcomers. We explore the case of the translation of the NGE as a culturally situated practice where community translators faced the particularities not only of the material translated, but also of the local context and target communities. Our research suggests that the process of the NGE’s translation not only empowered translators to make appropriate choices for their local communities, but also developed strategies for elevating the quality of the final product.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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, 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".