Bibliographic record
Abstract
2019 was a historic year for political struggle in Hong Kong. What had begun as protests against a bill that would allow for the extradition of activists and labour organizers to the mainland Chinese criminal justice system, transformed into a broader movement against authoritarianism and policing. A praxis embodying social work abolitionism has emerged in this context, with growing recognition that social service and healthcare sectors are closely intertwined with policing in Hong Kong and are in no way neutral bodies. This recognition has led to abolitionist solutions. Mutual aid practices have been highly visible in the protest movement, from protest supply stations to aunties sneaking into occupation sites to cook food for student protesters. The distrust of hospitals has led to the development of underground clinics, and activists have sheltered and cared for children kicked out of homes due to political differences with their families, creating networks of politicized chosen families. These seeds of abolitionism that have emerged in Hong Kong show that challenges to the carceral system are not exclusive to the West and social work abolitionist analysis and activism cannot be limited in geographic scope. Keywords: Abolitionism, Social Work Abolitionism, Hong Kong, Hong Kong Protests
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.004 |
| 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".