Competing globally, marketing locally: Subnational migration marketing in Australia and Canada
Bibliographic record
Abstract
Abstract In an era of skills-focused immigration, subnational units increasingly assert their role in attracting the ‘best and brightest’ migrants, creating a complex landscape of vertical (national vs subnational levels) and horizontal (among subnational units) competition. This article investigates the marketing tools and strategies employed by subnational units in Canada and Australia in competing for migrants. Adopting a subnational comparative approach, the study examines eighteen subnational units across both federal states, utilizing official immigration websites, migration plans, strategy documents, and immigration streams. Qualitative content analysis reveals that subnational units use sophisticated marketing tools, including comparisons and rankings, dedicated websites, videos, overseas missions, and employer resources. This marketing is not merely supplementary to national efforts; subnational units create distinctive narratives and policies that appeal to specific groups, differentiating themselves from other units and even from the central government. These units leverage local advantages, target specific migrant groups, and adapt their strategies according to their population size, migrant attractiveness, and regional needs. I argue that subnational migration marketing shows competition for desired migrants extends inwards from national borders as subnational units develop their own strategies. Subnational migration marketing transcends traditional nation-centric approaches, demonstrating the importance of localized, niche-focused, and competitive strategies in influencing not only who arrives, but where they settle, ultimately impacting regional development and addressing internal population distribution challenges. The findings underscore the distinctive nature of subnational migration marketing, as subnational governments actively differentiate themselves from the federal level and from other units to shape migration flows and policies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".