Bibliographic record
Abstract
In order to reduce preventable maternal mortality, it is necessary to go beyond ensuring the development and availability of effective health interventions. What is needed is a recognition that maternal mortality is caused by women's inferior social status and that women's disempowerment from birth represents a cumulative social injustice that governments are obliged to remedy through application of their political, health, and legal systems. The challenge of effectively applying such a human rights perspective to safe motherhood is similar to that required in efforts to eliminate slavery or racial discrimination: the necessary reforms threaten conventional practices and value systems. The claim that safe motherhood is a human right will gather legitimacy when it is understood that denying this claim creates an injustice within the standards of fairness that societies hold dear. In addition, countries must recognize that this human rights claim arises from their own cultural values. Then, governments must be held accountable. Advancing safe motherhood through human rights will require a diagnosis of laws, policies, and social norms. The task must include inquiries into the nearly 600,000 annual maternal deaths, and it must meet the challenge of translating human rights into the rights of each person to be human. As 1998 celebrates the first 50 years since the 1948 UN Universal Declaration of Human Rights, the next phase in human rights development must focus on the previously neglected interests of women.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".