Bibliographic record
Abstract
This chapter addresses ESL assessment in North America, covering both Canada and the USA. Even though the countries share commonalities, they each have a specific and different mandate regarding ESL assessment, linked to the official status of English in each country. Taking this into consideration, the chapter starts by discussing the issues of ESL terminology and defining ESL assessment: that is, what is ESL? And, what is considered as ESL assessment? Terminologies are in themselves problematic given the diversity of stakeholders and the interests connected with ESL teaching, learning, and assessment. Following this, the chapter discusses the legislation that governs language use within Canada and the USA. The different levels and groups of assessment population are described and differentiated from one another. Three main assessment areas are covered: for immigrant purposes, for educational purposes, and for work purposes. A brief historical background is provided to contextualize and define each of these categories. A range of English tests, either large‐scale standardized tests or institutionally developed, is currently used across Canada and the USA for various purposes. This chapter also outlines current ongoing research and identifies pressing issues and challenges that may dictate future research and professional directions for ESL assessment in North America.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".