Bibliographic record
Abstract
Twenty-five North America was familiar terrain to students and teachers alike. Predominately first-language students were enrolled in contentbased courses. Students expected to read books, think critically, write essays, and take exams. They expected their teachers to guide them through the challenges of Shakespeare, Anglo-American literature, and the complexities of the research essay. Teachers crafted curricula and lesson plans to achieve these goals. At the turn of the millennium, the high school English classroom in many metropolitan schools from San Francisco to Montreal is a very different place. A striking change is the dramatic increase in students from other cultures who speak languages as diverse as Chinese and Urdu, or Russian and Spanish, and whose last homes might have been in cosmopolitan Hong Kong or in a refugee camp. Minority groups who had been known as part of the metaphors of melting pots and vertical mosaics in sociology textbooks now walked into English classes as visible, audible realities. The case of Toronto gives some idea of the dramatic changes in urban populations over the last 25 years. Until 1961, 9 out of 10 immigrants came from Britain and Europe under a highly selective immigration policy that favored skilled, healthy immigrants. Nonwhites, now referred to as visible minorities, made up 3% of Toronto's population at that time (Siemiatychi, 1998). Changes in immigration policy, new trade alliances, and wars in Africa, Vietnam, and India ©2001 International Reading Association (pp. 440-449)
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.020 |
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".