Bibliographic record
Abstract
New Zealand’s immigration policy has undergone important changes in the past several years. The 1990s saw short-lived surges amounting to the highest net migration gains in over one hundred years (the so-called “Asian invasion” of the mid-1990s), some of the highest net migration losses of New Zealanders on record (the oft-noted “brain drain” of the late 1990s), and belated recognition that much of what is called “permanent and long-term migration” is not, in fact, permanent or long-term at all. (Bedford et al., p. 1) New Zealand’s future economic success is uncertain because it lacks within its current population some of the labor and technological skills needed to sustain economic growth. It is necessary, then, that it maintain an immigration policy that works to import these skills, logically from its skilled neighbors: Asians and Pacific Islanders. New Zealand has been and continues to be largely accepting of peoples from the United Kingdom, the United States, and Canada. However, New Zealand remains constantly uninviting to the “others” — its Asian neighbors — despite the considerable skills that they possess. Even though its immigration policies and initiatives have changed to no longer prohibit Asian immigration, attitudes toward settlement have not. It is because of this latent xenophobia that New Zealand’s immigration policy is arguably the country’s most contentious social issue. Each shift in policy has been met with harsh anti-immigration backlash and debate. (Grbic, p. 1) In fact, the unintended consequence of two immigration THE FOLLY OF LOOKING ONLY IN THE MIRROR Ashley E. Pritchard 1
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".