EXPLOITATION OR EMPOWERMENT? ADOLESCENT FEMALE DOMESTIC WORKERS IN UGANDA
Bibliographic record
Abstract
Women’s participation in public employment spaces has emerged with new modes of domestic gender hierarchies, especially in the Global South. A new direction in child labor, especially in the domestic sphere, negatively affects female children. Drawing from a larger study that examined the experiences of employers and employees in domestic spaces, this article examines experiences of live-in adolescent female domestic workers, commonly referred to as “house girls”, in Kampala, Uganda. Data were collected using qualitative methods that included individual interviews and life stories. Study findings reveal competing narratives pointing to both empowerment and exploitation of house girls. Using a feminist intersectional approach, I focus on gender, age, and location to trace the opportunities, challenges, and agency of the house girls. Using the household as the basic unit of analysis, I foreground the voices of house girls as they tell their lived experiences and survival strategies. My central argument is that domestic workers make a crucial contribution to the effective participation of women working outside the home. I conclude that sustainable empowerment of workers, both domestic and outside the home, hinges upon changes and transformations at the household level.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".