Bibliographic record
Abstract
<p dir="ltr">This report provides a comparative insight into regional ethnic diversity across five countries. We include regional/provincial site studies for each country with accompanying data visualisations from the nearest metropolitan cities to these regional/provincial sites. With a focus on both national and localised regional policies to manage regional migration, we present findings from desktop reviews for each site to understand ethnic diversity alongside overarching findings from the comparative approach.</p><p dir="ltr">The NoE was originally funded to conduct a pilot project that involved fieldwork at one regional site per country for data collection of relevant policy and practice documents and other data to develop the site reports for each location. The COVID-19 pandemic significantly impacted the original NoE pilot study approach, restricting opportunities for regional fieldwork in Australia, Canada and Europe. In light of these impacts, the NoE team worked together to redesign the pilot project plan. The project was revised to focus on desktop research with a data visualisation output. This has resulted in the inclusion of additional regional sites from the three European countries, two Canadian locations and two Australian locations (see Table 1).</p><p dir="ltr">The pilot study was revised to address the following objectives, guided by the research questions presented below.</p><p dir="ltr"><b>Objectives</b></p><ol><li>To better understand the intersection of migration policies and changes in non-metropolitan cities and towns in five countries of interest (Australia, Canada, Italy, Germany and the Netherlands).</li><li>To examine changes to ethnic diversification, and the ‘liveable diversity’ of regional areas over the last 10 years, and how this differs across national contexts.</li><li>To consider the ways non-metropolitan areas are responding to changes in diversity as reflected in local strategic policies and planning (or indeed their absence).</li></ol><p dir="ltr"><b>Research questions</b><br>• How do the macro migration policies differ in our countries of interest as they relate to primary and secondary migration to non-metropolitan areas?<br>• How have non-metropolitan areas changed in their socio-demographic compositions in response to broader changes in macro migration policies?<br>• At the local level, how are social, economic and/or cultural aspects of these non-metropolitan areas reflected in local policies and practices?</p><p dir="ltr">Let me know if you need the formatting adapted to a specific style guide (e.g., APA, Chicago, MLA).</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".