Bibliographic record
Abstract
This chapter focusses on my research extending back to the early 1990s, addressing in different contexts the gendered and racialised work of care within families and its relation to women’s paid employment. This research has explored how geographical proximity and distance are inextricably intertwined with unequal relations of gender and socially contingent ethno-racial difference. My early career work focused on the links between unpaid work in the home and women’s labour market participation in Worcester Massachusetts. Later research, mostly in Vancouver, Canada, and the Philippines, has explored the intersections of race and gender in the global market of commodified care, in which women from the global South migrate to the global North to care for families there, along with their families back home. The research has used various methodologies, from standard social science surveys to writing plays for theatrical performances, almost always in collaboration, not only with other academics, but with the communities whose stories are being documented. Collaborations, I argue, are good to think with and through, in part because they can put the white scholar in her place, as located and accountable to her location. What’s near and far matters immensely to people’s life trajectories. Understanding how proximity and distance matter requires close attention to the researcher’s own context and positionality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".