“Just Do It!” - Celebrating Plurilingual Writing in an Undergraduate Writing Contest
Bibliographic record
Abstract
The Simon Fraser University Student Learning Commons holds an annual interdisciplinary, under-graduate writing contest. The writing contest provides an exciting opportunity to chal-lenge deficit frameworks within writing and academic success centres. Through the contest, the writing centre is empowered to actively seek out and showcase excellence in under-graduate writing. Throughout the five years of the contest, the organizers have tweaked the contest’s submission categories to reflect the needs and interests shared by students and faculty member, to ensure that thecontest supports the centre’s larger goals. This paper des-cribes the creation of the contest’s Plurilingual Prize category, emphasizing the ways that this prize advances the writing centre’s commitment to both linguistic diversity and linguistic justice. The paper also provides context for the decision to use the term plurilingual to describe this contest category, as opposed to other terms used in writing centre discussions, such as English Language Learner (ELL), English as an Additional Language (EAL), English as a Second Language (ESL), and multilingual learners.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".