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
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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