Maintaining competitiveness in the global market for skilled migrants: an examination of skilled migration narratives in Canada, Australia, the USA, and India, and global use of technology to manage and select economic immigrants
Bibliographic record
Abstract
Competition for skilled migrants between nations is not new, but is increasingly fierce. It presented an especially significant challenge for traditional immigration recipient countries (including Canada, Australia, and the USA) in the decade preceding the Global Financial Crisis, given the entry of many other OECD member nations to the global skilled migration market, and free movement of persons within the EU. A mission for states, therefore, in an era of relative hypermobility, was to find ways to attract and retain skilled labour in a highly competitive, lucrative and fluid international marketplace. This thesis engages with an overarching research question of how states can maintain competitiveness in the global market for skilled migrants, by first considering the issue theoretically, and then empirically from the perspective of the aforementioned receiving states, a sending state, and migration apparatus and systems. It concludes by reflecting upon contemporary developments and conceptual matters, with a view to remarking on the future of global competition for skilled migrants, and international immigration generally. Original data underpins the empiric papers of the thesis, collected in key cities of recipient countries (Vancouver, Ottawa, Washington DC, Toronto, Adelaide and Canberra) as well as from a significant labour sending state, India (in New Delhi and Chandigarh). In each of these cities, comparable primarily qualitative data was gathered from interviews with key migration stakeholders, including: immigration processing staff and migration policy-makers in government; employees of private migration agency firms and professional accreditation bodies; and politicians and academics involved in research on skilled migration and global labour competition. Primary data was also acquired from a global survey conducted in partnership with the world’s largest specialist immigration law firm, examining the extent to which countries utilise technology in the management and selection of economic migrants. The thesis proposes a new general model for economic migration policy, and contends that countries can most effectively compete in the global market for skilled migrants by looking beyond servicing their immediate domestic labour-market demands, facilitating transitions from temporary to permanent residence, centralising immigration administrative mechanisms, and removing barriers to social and economic integration. It concludes by reflecting upon the degree to which states might continue to compete for the ‘best and brightest’, and situates the relevance of the work presented, within Geography, and migration scholarship more broadly.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".