Enhancing learning through building on students' and parents' linguistic and cultural repertoires in year 1 classrooms
Bibliographic record
Abstract
Australia is fast becoming one of the most diverse nations in the world. Recent Australian Census data (Australian Bureau of Statistic, 2016) has revealed that 2.8% of Australians are Aboriginal and Torres Strait Islander peoples and 49% of Australians were either born overseas or have at least one parent born overseas, a proportion higher than the United Kingdom, Canada, New Zealand and the United States. Data has also revealed that Australians come from nearly 200 countries around the world, identify with over 300 different ancestries and speak more than 300 languages. Approximately 21% of Australians people speak a language other than English at home. Although unevenly distributed, many Australian schools reflect this diversity and include students who speak many different languages and dialects of English. These students draw on multiple ways of learning and understanding and like many young people around the world they are increasingly mobile and connected across time and space.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".