Bibliographic record
Abstract
At no other time in the past century has there been such focused and intense global interest in international migration. Never before has there been such interest, internationally, in how Australia, Canada and New Zealand manage their international migration. These countries have become models for governments elsewhere who are seeking to develop policy that has a more direct impact on the quality of the population flows into their countries. \n \nNew Zealand is unusual by OECD standards in that it has a high level of emigration of citizens at the same time that it has a very high per capita rate of immigration. New Zealand’s contemporary migration flows are examined briefly and it is demonstrated that the system is not nearly as dominated by migration from countries in northeast Asia as it was a decade ago. \n \nA more flexible approach to the attainment of permits to reside in a country is being adopted in most countries now. The prospective migrants take the opportunity to assess employment opportunities and the quality of life in a prospective new home (perhaps not their only home either), while working or studying on temporary permits and gaining the sort of local experience that is valued in the points-based immigrant selection systems. The paper concludes with a brief analysis of data relating to transition to residence in New Zealand.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".