Mother Tongue as the Language of Mothering and Homing Practice in Betty Quan’s Mother Tongue and Hiromi Goto’s Chorus of Mushrooms.Survival Strategies and Identity Construction of Migrant and Refugee Mothers
Bibliographic record
Abstract
“I am my language. I speak Chinese. Your voices. Your words. You drown \nme out” (Quan 14) \n– \nsays the Mother to her children in Betty Quan’s play \nMother \nTongue \nunderlining \nthe cru \ncial aspect of mothering in exile, the language of \nmothering \n, \nas a site in which the cultural/ethnic identity, the sense of self and \nmaternal subjectivity \nintersect and \nare negotiated. This chapter explores the language \nof mothering through two fictional a \nccounts of immigrant mothering both of which \nunderscore \nthe tension between mothering practices and linguistic difference. It aims \nto address this tension by suggesting that in the context of migration, the language of \nmothering can be conceptualised as a \nmothering and homing practice, which \nfunctions as a structuring, creative force that produces and shapes both migrant \nmothers’ and their children’s subjectivities. The chapter will consider two fictional \ntexts: the play \nMother Tongue \n(1996) by Chinese \n- \nCana \ndian playwright Betty Quan \nand the novel \nChorus of Mushrooms \n(1994) by Japanese \n- \nCanadian author Hiromi \nGoto \n, \nboth featuring immigrant and refugee mothers who mother primarily in their \nown mother tongue rather than English, the language of the host country, \nCanada.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".