Academic Language in K-12: What Is It, How Is It Learned, and How Can We Measure It?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.219 | 0.000 |
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 it