ISFIRE 2009: International Symposium for Innovation in Rural Education: Innovation for Equity in Rural Education
Bibliographic record
Abstract
The concept of an international symposium on rural education arose from a meeting between members of the SiMERR National Centre, Australia and the NURI Teacher Education Innovation Centre (NURI-TEIC) at Kongju National University, Korea in 2007. Despite the very different national contexts, the teams were struck by the similarities of the challenges facing rural schools in the two countries, and curious about the degree to which these challenges were shared by other countries. At a subsequent meeting in Australia in December 2007, the two centre Directors - Professor John Pegg (SiMERR) and Professor Youn-Kee Im (NURI-TEIC) - agreed on a framework for the first International Symposium for Innovation in Rural Education (ISFIRE). This volume consists of the keynotes and refereed papers presented at ISFIRE 2009. The papers provide insights into rural education in Australia, Bhutan, Canada, Korea, Norway, South Africa and the United States along the following themes: 1. Promoting rural policy initiatives; 2. Nurturing the rural teacher experience; 3. Enhancing rural student experience and growth; 4. Optimising the curriculum; 5. Improving resources in rural schools; and 6. Addressing special issues in rural education.
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".