Narratives as Catalysts for Transformation and Social Action Planning within the Hong Kong Indonesian Migrant Community
Bibliographic record
Abstract
This paper discusses the use of written and oral narratives, composed as classroom assignments by adult Indonesian migrant workers, sojourning in Hong Kong. Individually written narratives embody group-common elements that can be acted upon, thus becoming catalysts for personal growth and for group social action planning. Personal growth includes refocusing personal identity away from the societally imposed and devalued ‘domestic helper’, toward identities that offer self-empowerment. Redefining personal identity within a group learning situation also builds group identity which can be directed toward confronting hegemonic forces. This is done on four fronts: firstly, by claiming the symbolic right of cultural space and by demanding respect within the larger Hong Kong community; secondly, by publicly agitating against government policies, such as human rights and minimum wage legislation, that migrant workers believe disadvantage them; thirdly, by increasing ability in English, Cantonese, and basic computer applications that specifically meet Indonesians’ work requirements and interests; and finally, by building the capacity to confront employers in claiming government guaranteed minimum wages and rest days. Through these actions, the opportunity for both personal and societal transformation is created.Data was collected through journal narratives and semi-structured qualitative interviews with migrants who were taking weekly English languages classes at a small private training center in Hong Kong.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| 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".