Researching and reporting on international teacher education agendas: signalling change
Bibliographic record
Abstract
Teaching and teacher education are easy targets when it comes to showing cause for falling levels of student learning achievement in schools across a number of nations worldwide. Australia is a case in point, where the 2015 Programme for International Student Assessment report on educational performance from 72 countries worldwide shows that the learning attainment scores of Australian 15 year olds in mathematics, science, and reading is slipping in comparison to the test scores of young people in countries such as Japan, Canada, and New Zealand. The recently released 2016 National Assessment Program-Literacy and Numeracy data also show a plateauing of results in literacy and numeracy by Australian students since the introduction of the annual test in 2008. While one might argue against these types of measures of student achievement on a number of fronts, they nonetheless carry high stakes for teacher practice and teacher education, as evinced through wide coverage and often strident debate in academic publications, political and policy commentaries, and social and mass media.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".