Gender, ‘race’, poverty, health and discourses of health reform in the context of globalization: a postcolonial feminist perspective in policy research
Bibliographic record
Abstract
Gender, ‘race’, poverty, health and discourses of health reform in the context of globalization: a postcolonial feminist perspective in policy research In this paper, I draw on extant literature and my empirical work to discuss the impact of globalization and healthcare reform on the lives of women — those from countries of the South as well as of the North. First, I review briefly the economic hardships identified in different sectors of the population that have been attributed to how globalization is now working. Second, I examine what these global processes mean for health, with particular focus on poverty, gender, racialization and health. Third, I reflect on how nurse scientists might develop research agendas in the 21st century that would foster social transformation and social justice for all people. The position taken here is not an indictment of globalization. Rather, I argue that globalization is a fact in all of our lives. There are positive aspects of globalization. There are also negative aspects which we must collectively address, given that the issues identified can have deleterious consequences for the world’s poor, women in particular. I suggest that, to construct knowledge for practice and praxis, research agendas of the future should be inclusive of subaltern voices. I argue that drawing on a postcolonial feminist epistemology might help us to define such agendas, and express the multilayered sociopolitical contexts of health and illness in advocacy with policy‐makers.
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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 | 0.027 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.091 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".