Two-Way Immersion Programs: Features and Statistics
Bibliographic record
Abstract
The first TWI program in the United States began in 1963. For the next 20 years, the growth of TWI programs was minimal, with fewer than 10 documented programs in operation before 1981. The majority of programs in existence today were established during the past two decades. The 2000 Directory includes 248 TWI programs in 23 states and the District of Columbia. There has also been considerable expansion within existing programs: Many have reported adding new grade levels each year, and 40 programs now extend into middle or high school.Program location. The majority of TWI programs are in public schools; only four are operated by private schools. Nearly a quarter of the public school programs operate in specialized environments: 11 are housed in charter schools and 53 in magnet schools. California has the most programs operating in specialized environments, with eight charter school programs and 22 magnet school programs. Relatively few TWI programs (32) are whole-school programs. About three quarters of the elementary programs (191) operate as strands within schools, as do all of the secondary programs (32). Twenty-five programs did not respond to this question.Languages of instruction. Most TWI programs are Spanish/English (234). The other programs are Chinese/English (5), French/English (5), Korean/English (3), and Navajo/English (2). (One school houses both a Spanish/English and a Chinese/English program.) The majority of students enrolled in these programs are native speakers of one or both languages of instruction. In 37 programs, however, more than 1% of the students are native speakers of a language not used in the program (i.e., third language speakers). In nine programs, 5% are third language speakers.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".