Bibliographic record
Abstract
Malta is a small predominantly Catholic island-nation in the Mediterranean ocean and there is a complete ban on abortion in the country – there are no exemptions for rape, incest, fetal anomalies, or to save a pregnant woman’s life. Both medical and surgical abortions are illegal and women choosing to end pregnancies have often sought to have abortion care outside of Malta, traveling primarily abroad at great personal cost. Medical care in Malta is otherwise free at the point of care. Options for the care are extremely limited as there is great public support for the Maltese abortion ban. As the government of Malta imposed travel restriction in the interest of containing spread of SARS-CoV2, women faced further limitations in their ability to access safe, effective abortion care in other countries. Many women have ordered medications online and have self-managed their abortion care. Women who have had abortions or people who facilitate abortions in Malta do face criminal charges and women who seek medical management of complications of self-managed abortions do so where the potential of charges exists. Denying women access to safe and private abortion care has the potential to cause significant physical, psychological, and social effects and unfairly harms those who are experiencing unwanted pregnancy. Given the global COVID pandemic, the Maltese government has a responsibility to ensure access to safe abortion care whether abroad or self-managed and such care should be decriminalized.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.247 | 0.051 |
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".