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Record W3126659668 · doi:10.26522/ssj.v15i1.2043

Examining Gender and Resistance with Filipina Hong Kongers through Cellphilm Production and Collaborative Writing

2021· article· en· W3126659668 on OpenAlexafffundvenue
Casey Burkholder, Jianne Soriano, Alecxis Ramos-Pakit

Bibliographic record

VenueStudies in Social Justice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGender studiesResistance (ecology)SociologyFilmmakingEthnic groupGirlMedia studiesIdentity (music)White (mutation)PoliticsCitizen journalismPolitical sciencePsychologyVisual artsAestheticsLawAnthropologyMovie theaterArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.384
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes3
Has abstractyes

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