One Quarter of Californiaâs Teachers for English Learners Not Fully Certified
Bibliographic record
Abstract
Although the passage of Proposition 227 reduced the demand for bilingual teachers, an acute shortage of teachers qualified to deliver needed instructional services to English learners remains. In 1998, prior to the passage of 227, 43 percent of the teachers providing instructional services to English learners were not fully certified to provide those services—33 percent of teachers were in training to provide English language development (ELD) or Specially Designed Academic Instruction in English (SDAIE) and 10 percent were in training to provide primary language instruction. By 2001-02, 25 percent of teachers providing instructional services to English learners were not fully certified. Statewide, almost 14 percent of all teachers do not hold a full credential. So English learners are almost twice as likely as students generally to be taught by a teacher who is not fully certified. That figure is even higher if you include another 14 percent of teachers who have other than a California Teacher Commission (CTC) authorization, which can be obtained with less rigorous training through a SB1969 certificate or a district designation.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".