Scaling the Great Wall of Canada: Technological Solutions for More Accessible and Equitable Language Proficiency Testing
Bibliographic record
Abstract
This presentation refers to the current language testing regime as "The Great Wall of Canada" to describe the barriers of cost, time and effort newcomers face, caused by language proficiency testing. The presenters will introduce a machine learning and data-driven software application, arising out of our research in language test variability, which aims to reduce the burdens of cost and time to newcomers, Through the predictive capability of machine learning algorithms, users of the application may access an estimate of relative success in language proficiency test type, test score, test location, and test preparation. This presentation will introduce the research background and context leading to the development of the software application. An overview of machine learning (ML) technologies will follow, accompanied by an explanation of how ML functions in recognising predictive patterns in newcomers' self-reported language test data. The presenters will also discuss critical questions regarding ML's predictive capacity for language test takers and test users, including algorithmic bias in digitizing linguistic prejudice, and matters of data privacy.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".