2.21 BILLION REASONS: Creating Safe Environments for Children
Bibliographic record
Abstract
»NO VIOLENCE AGAINST CHILDREN IS JUSTIFIABLE; ALL VIOLENCE AGAINST CHILDREN IS PREVENTABLE.« Violence touches everyone; it either hides behind the closed doors of homes and institutions, or it permeates every aspect of life through war and conflict. It is a daily reality for millions of people around the world, affecting all ages and both genders within every social context and nationality. Violence is a complex problem related to patterns of individual thought and behaviour that are shaped by a multitude of forces within relationships, families, communities and societies. It is a health, social, justice, legal, economic, spiritual, development, risk management, and human rights issue. Although violence impacts members in every community and society, children and youth are the most vulnerable. In every part of their lives – their homes and families, schools, institutions, workplaces and communities – children are beaten, sexually assaulted, tortured, neglected, maimed, bought and sold, and killed. Far too often the adults in their lives are the perpetrators of their pain or the »observers« and take little or no responsibility to protect them and create safe environments. The consequences are enormous – at the individual, family, community, and societal levels.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".