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Record W7122783535 · doi:10.1093/migration/mnaf058

Competing globally, marketing locally: Subnational migration marketing in Australia and Canada

2025· article· en· W7122783535 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMigration Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec
KeywordsImmigrationLeverage (statistics)Distribution (mathematics)PopulationCompetition (biology)

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.323
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it