Ukrainian Language Education Network: A Case of Engaged Scholarship
Bibliographic record
Abstract
The study explores one longitudinal case of engaged scholarship, the collaborative practices in the Ukrainian language educational network from the 1970s to the present. The focus is on the Ukrainian Language Education Centre (ULEC) at the University of Alberta, which over almost four decades has worked with the community in the development of Ukrainian education by keeping approaches to language learning and its use on the cutting edge of practice. Over the years, ULEC engaged with the community seeking to respond to the community’s needs. Past and present practices of ULEC and its partners are studied through the prism of the engaged scholarship framework (Boyer, 1996; Barker, 2004; Sandmann 2008, 2009). These practices are analyzed through three strands of engagement: purposes, processes, and products, which are defined, explored, and discussed. The study also describes engaged scholarship projects related to Ukrainian language education currently being conducted by ULEC, with a focus on collaboration with communities in the production of knowledge and their potential for strengthening a network of reciprocity.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".