Academic Language in K-12: What Is It, How Is It Learned, and How Can We Measure It?
Bibliographic record
Abstract
Using Beck, McKeown and Kucan’s (2002) three-tiered model as a general framework for vocabulary, this article sheds light on the construct of academic vocabulary and how it might be measured. Using illustrative writing samples of student work of different genres and drawing on corpus based studies, I highlight the distinction between general, high utility academic vocabulary visible in expository mode (Tier 2), and narrative vocabulary (Tier 1) on the one hand; and general, high utility academic vocabulary and literary vocabulary associated with English Language Arts literature-based curriculum (Tier 3). Tier 2 words have consistently correlated with academic achievement across disciplinary boundaries, especially at post-secondary levels. Suggestions are made for policy reform, curriculum, and assessment approaches that will afford more equitable access to post-secondary programs of study for the growing numbers of English language learners across Canada, including in provinces such as Alberta and British Columbia.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".