Bibliographic record
Abstract
Negotiating Citizenship: Migrant Women in Canada and the Global System, Daiva K. Stasiulis and Abigail B. Bakan, Toronto: University of Toronto Press, 2005, pp. 233. Negotiating Citizenship is a thoughtful, well-researched and insightful book concerned with the topical issue of citizenship and contesting citizenship rights within the context of global capitalism. It is a timely and comprehensive piece that interrogates and dissects traditional theories of citizenship, and offers an alternative theoretical perspective on the basis of examining the status and condition of foreign domestic workers and nurses who migrate to Canada from two similar but different regions of the world: the Caribbean and the Phillipines. In creating the context for discussion, the work takes the reader on the journey of these migrant women workers from their home countries in the “Third World” where national labour markets are becoming increasingly unable to absorb surplus labour and where they have been adversely affected by the anti-social structural adjustment policies of the International Monetary Fund (IMF), to the more economically developed Canada where the state encourages them to work without providing them with citizenship rights. By so doing, as the text points out, the Canadian state, which still has primacy under globalization, reinforces the vulnerability of an already vulnerable social group for which migration is a survival strategy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.039 | 0.021 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".