Collaborating to safeguard children in Taiwan: Systemic transformation
Bibliographic record
Abstract
Child abuse and exploitation pose significant threats to the health and well-being of children. While the Taiwanese governmentintroduced the Protection of Children and Youth Welfare and Rights Act in 2011 to address these issues, progress has been slow. This paper aims to examine the evolution of Taiwan’s child protection system (CPS), with a particular focus on interdepartmental collaboration. Through the collection of legislation, statistics, conference proceedings, and reports, this study analyzes the working model between law enforcement and public health. Three cases of collaboration between law enforcement and public health at the community level are presented: social safety net programs, early intervention for child abuse, and trauma-informed training for first responders. The accomplishments and challenges of each project are discussed, along with a review of the CPS in relation to the United Nations (UN) strategy INSPIRE’s approaches. Although Taiwan has shown a commendable emphasis on prevention and family support, the collaboration between law enforcement and public health is still in its early stages. The next crucial step is to strengthen integration in the early stages of identifying, assessing, and referring cases of child abuse and neglect. This can be achieved by generating more evidence on effective working models and promoting their implementation.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".