Mental load at the intersection of migration, motherhood and work
Bibliographic record
Abstract
Abstract This paper investigates the narratives of Mental load (ML) within the realm of migration. The study captures the migration experiences of three Indian mother‐workers across their journey of migration. By examining the ML situated at the intersection of migration, motherhood, and paid work, our study bridges the theoretical gap at the micro level by understanding how skilled Indian mother‐workers manufacture subjectivities as they now spend their lives in Australia and Canada. We define ML in the context of migration and explore how these women navigate the newness of identity, cultural adaptation, and reframe mothering, all while juggling their ML accompanying the unfamiliarity of mobility. Further, we demonstrate how migrant mothers understand themselves diversely in relation to their careers in the new land. We find that ML ascends in the beginning of the journey. Further, the research unveils that the mother‐workers agentically modulate their ML with a clear and well‐defined migration objective as the guiding beacon in steering through the subsequent migration journey. Moreover, the absence of clarity in migration objectives substantially augments the ML. These results hold significance in the conceptualization of migration‐related ML of mother‐workers, hence offering a subjective lens to capture the everyday portrait of a migrant mother‐worker.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".