In this article: • Who are SIFEs? • Which countries do SIFEs come from?
Bibliographic record
Abstract
• Classroom Support for SIFEs "I didn't know how to read in my own language. I didn't know how to write. I only know how to speak and how to go to work." — Johnny Romah, Student Readers of The Vancouver Sun recently had the opportunity to meet Johnny Romah, a student who had survived a treacherous journey and experience in a refugee camp before coming to Canada. Darah Hansen, the reporter who covered the story, wrote, Johnny Romah was 17 on his very first day of school. A member of the Montagnard — or mountain — people of Vietnam, Romah arrived in Vancouver in 2005 after an exhausting year spent in a refugee camp in Cambodia. He'd fled his impoverished village at the age of 16, fearing arrest — even death —at the hands of Vietnamese government forces, which have long been in brutal conflict with the country's indigenous people … Before coming to Vancouver, he'd never spent a single day in a classroom, never read a book, and only once, in the refugee camp, could he recall ever attempting to put pen to paper. (2008) Johnny is one of 200 refugee students in the Vancouver school district, and is what many educators consider a Student with Interrupted Formal Education (SIFE). Even if Johnny had known how to read in his native language, the process of
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".