Effective practice in EAL education: enacting distributed school leadership
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
This article reports on research conducted in New South Wales, Australia, which investigated why students from linguistically and culturally diverse backgrounds in some schools achieve consistently higher levels of educational growth than students in other schools with similar demographics. The purpose of this research was to learn more about high growth schools, and the factors that contributed to their students’ educational success. Based on analysis of data from six high growth schools, the article argues that the intersecting elements of building constructive relationships within and between staff and students; of valuing expertise in English as an Additional Language (EAL) education; building teacher professional knowledge; and promoting systematic school-wide implementation of EAL pedagogical principles were pivotal to students’ successful educational outcomes. However, it also argues that the effective implementation of these factors is dependent on the nature and quality of leadership in the schools – a leadership that recognises and values these factors in school-wide programs and provides space and support for their implementation.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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