Bibliographic record
Abstract
This research examines the experiences of female domestic workers in Calcutta, India, with a focus on the relationships between the workers and their female employers. Semi-structured interviews were conducted with thirty female domestic workers in Calcutta in 2015–2016. The women were asked to reflect on their relationships with their female employers, and responses were analyzed using grounded theory. Three themes emerged from the analysis, indicating three different kinds of relationships the workers had with their employers. Some of the women talked about a professional relationship, which is formal and lacks warmth, characterized by status differentiation. Others talked about a distant and abusive relationship, where exploitation is direct and explicit. The largest group talked about a caring and supportive relationship, where there is love, consideration, and understanding. All three groups of women, however, felt that their relationship with their female employer involved some level of exploitation, whether it was overt or concealed. These findings give voice to a vulnerable group of workers in contemporary India, exposing social relationships between women of different classes that are characterized by both compassion and exploitation. We argue that these women’s stories provide further evidence that legal protections must be put in place for domestic workers in India, in accordance with international recommendations.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".