Myths and Delusions: English Language Instruction in Canadian Schools.
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
LIKE THE PROVERBIAL ELEPHANT IN THE MIDDLE OF THE LIVING ROOM THAT everybody walks around, the state of ESL in Canada has been a looming, mishandled entity. Canadians espouse the benefits of diversity and have politically correct policies concerning racism and equity for the linguistically disadvantaged, but in reality something has gone terribly wrong. As Larry Bourne, professor of Urban Studies at the University of Toronto has said, “The scale of changing ethnicity and language demographics has been absolutely staggering... and everybody, especially the schools, are struggling to keep up.”1 With this polyglot clientele, teachers must educate students of different languages, cultures, religions, and proficiency levels in English. This article outlines specific myths and delusions that plague educational institutions as they struggle to respond to the challenges of diversity.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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