Bibliographic record
Abstract
For this virtual special issue, 12 of the 15 papers 1 selected from those published in Geographical Research over the last 12 years (2006-17) have been authored primarily by academics based in Australia.The papers reflect geographers' enduring engagement with migration as a phenomenon that cuts across different spatial scales.Until recently, migration studies published in the journal were overwhelmingly concerned with internal movements within the Australian continent.There are signs that the locus of interest is shifting to broader geographical terrains in the past two years-within the pages of the journal what begins to emerge are studies of migration issues located in a variety of contexts ranging from the Norwegian High North to Bihar, one of the poorest states in India.The Australian focus of migration scholarship in Geographical Research is clearly the dominant pattern in the first half of the 12-year period under consideration.Yet, it is interesting to note that when Graeme Hugo-one of Australia's pre-eminent geographers and demographers best known for his sterling contributions to migration studies-published a paper in the journal, he chose to focus on Asia as the centrepiece in his analysis of the 'parametric increase in the scale and complexity of global international migration' (Hugo, 2006, p.155; see also Hugo, 2005aHugo, , 2009)).That paper grapples with the enormous economic, social, and political diversity in Asia, and reviews key developments in international labour migration in both the highly skilled and unskilled circuits, student migration, diaspora, and undocumented migration.In highlighting the striking 'circularity' in people movement
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.224 | 0.078 |
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".