Negotiating Openness under Authoritarian Risk: Feminist Open Data Sharing in Hong Kong
Bibliographic record
Abstract
Open data has become increasingly common in Science, Technology, Engineering, Mathematics, and Medicine (STEMM) in recent years, but its promise falters when the “data” are people’s stories, such as interview transcripts, field notes, oral histories, diaries, and images, which are typical of qualitative work in such disciplines as cultural studies, sociology, and anthropology. In this article, we reconsider open data sharing in politically precarious and authoritarian settings and explore the ways in which feminist and CARE-influenced approaches to data sharing can be operationalized in these regions. Grounded in two Hong Kong–based projects on asexual and aromantic (A-spec) community narratives and interviews around the 2019 Anti-Extradition Bill (Anti-ELAB) movement, we combine conceptual argument and autobiographical reflection to trace the tension between visibility and vulnerability, as well as between verification and the duty to protect. Rather than treating openness as a one-size-fits-all mandate, we recast it as negotiated, relational, and community governed. Engaging with existing scholarship in feminist and critical data studies, we propose steps to operationalize these feminist principles of open data sharing. This approach keeps interpretive integrity with those who lived the experiences, resists extractive reuse, and still enables learning and accountability. Set against Global South conditions, the article offers a practical, care-centered template for qualitative open data sharing that remains workable under political precarity and authoritarian constraint.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".