Bibliographic record
Abstract
In the global race for skilled immigrants, governments compete for workers. In pursuing such individuals, governments may incidentally discriminate on gender grounds. Existing gendered differences in the global labour market related to life course trajectories, pay gaps and occupational specialisation are refracted in skilled immigration selection policies. This book analyses the gendered terrain of skilled immigration policies across 12 countries and 37 skilled immigration visas. It argues that while skilled immigration policies are often gendered, this outcome is not inevitable and that governments possess scope in policy design. Further, the book explains the reasons why governments adopt more or less gender aware skilled immigration policies, drawing attention to the engagement of feminist groups and ethnocultural organisations in the policy process. In doing so, it utilises evidence from 128 elite interviews undertaken with representatives of these organisations, as well as government officials, parliamentarians, trade unions and business associations in Australia and Canada over the period 1988 through to 2013. Presenting the first book-length account of the global race for talent from a gender perspective, Gender, migration and the global race for talent will be read by graduate students, researchers, policy-makers and practitioners in the fields of immigration studies, political science, public policy, sociology, gender studies and Australian and Canadian studies.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".