Examining Gender and Resistance with Filipina Hong Kongers through Cellphilm Production and Collaborative Writing
Bibliographic record
Abstract
Hong Kong’s non-white ethnic minorities – including its Filipina residents – are often described in media and policy discourses as a unified group. Speaking back to this misconception, in this article we describe the gendered experiences of two 23-year old Filipinas born and raised in Hong Kong through what Claudia Mitchell has described as girl method – research with girls for girls and about girls’ concerns – in our case producing visual depictions of girlhood in cellphilms (cellphone + filmmaking + intention) and collaborative writing. We write together as co-researchers to extend participatory approaches to research dissemination as we make sense of the changing political situation in Hong Kong in the years since our first collaboration in 2015. Through a polyvocal – many voices writing together – reflection on a cellphilm production project on identities and belonging four years later, we argue that Filipina identity in Hong Kong is complex and multifarious, and we aim to disseminate knowledge by and for Filipina Hong Kongers that speaks back to the erasure of their experiences within larger discourses about Filipinas, gender, and activism 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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".