Bibliographic record
Abstract
In April 2023, the World Health Organization (WHO) issued new estimates affirming that one in six individuals experience infertility globally. Yet, many states are unclear on their responsibility to prevent infertility, ensure access to treatment, and to end the harm suffered by individuals who are considered infertile. Responding to this uncertainty, in June 2023, the United Nations Office of the High Commissioner on Human Rights (OHCHR) issued a new research paper explaining states' legal obligations regarding infertility. Importantly, OHCHR underscores that states must take steps to prevent infertility by addressing its root causes and ensure access to treatment. Further, states must address the negative consequences of infertility, including stigma and violence, as well as the discriminatory stereotypes that lead to certain groups facing disproportionate harm from infertility. This article provides an overview of the OHCHR report and explains what this means for healthcare providers, who have a critical role to play in providing care and advocating for legal and policy reform necessary to prevent, diagnose, and treat infertility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".