Note from the Editors: Australasian Journal of Regional Studies - Volume 20, Number 2, 2014
Bibliographic record
Abstract
This volume is testimony to the increasing diversity of Australian regional science and Australia's interest in, and engagement with, our Asian neighbours. From a domestic perspective the articles in this volume range across regional economic and social diversity in Victoria, the economic transformation of coastal communities as traditional fishing activities decline in importance for a variety of reasons, and small business innovation in farming communities during a period of intense drought. Our overseas contributors are equally diverse and fascinating. Here the focus is on three issues, all of which are likely in various ways to impact on Australia's regional wellbeing. They serially concern economic reform in China, reproductive health in Bangladesh, and income growth in highly heterogeneous Indonesia.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".