Bibliographic record
Abstract
SEVEN OAKS SCHOOL DIVISION IN WINNIPEG stepped into new but not totally unfamiliar territory for the opening of a new school this fall. The Seven Oaks School Division Met School, a high school that limits class size to 15, tailors its curriculum to the needs and interests of its individual students, places students in community-based internships two days a week, and keeps the teacher – called an advisor – with the same group of students from Grade 9 through graduation. It’s a new approach to learning that officials hope will pay dividends for students. Seven Oaks Met has enrolled 45 students in Grades 9 and 10, will add Grade 11 and expand to 90 students next year, and will reach a maximum enrollment of 120 in the third year when it adds Grade 12. By year three, with Grade 12 added, enrollment is expected to rise to a maximum of 120 students. The alternative school lives inside Garden City Collegiate, a north-end Winnipeg high school of 1,100 students of whom 20 percent are Aboriginal and about 30 percent visible minority, according to Brian O’Leary, Seven Oaks Superintendent. Seven Oaks Met is part of a network of alternative schools known as Big Picture Learning, which has its origins in Providence, Rhode Island. It was there that the Metropolitan Regional Career and Technical Center (the”Met”), a public school, opened in 1996 with a Grade 9 class of 50 mostly “at-risk” African American and Latino students. Four years later, 96 percent of that first class graduated and 98 percent of the graduates were accepted by postsecondary institutions. Many of those graduates were the first in their family to earn a high school diploma and 80 percent were the first in their family to enroll in college, according to Big Picture co-founders Dennis Littky and Elliot Washor. The Big Picture network has grown to include more than 60 schools across the U.S., as well as others in Australia, Netherlands, Israel, and now Canada. All share the view that students should take responsibility for their own education. “We reduced the class in the school to the lowest common denominator: the student,” says Mr. Washor. “It used to be teachers looked at a class and couldn’t see each and every student; we’ve managed to not just make class sizes smaller but develop a system where teachers can look at each and every student and know them very well.”
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.153 | 0.053 |
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".