Ideal citizens: the birthing of state truths and fictions in Quintana Roo
Bibliographic record
Abstract
Reducing the maternal mortality rate (MMR) is an important part of Mexico's commitment to the Millennium Development Goals, and the country has made great strides towards achieving this goal. However, researchers have questioned to what extent the focus on improved MMR and other indices of maternal health has contributed to an emphasis on improved statistics rather than quality care, and the effect this has had on the quality of reporting. While public health officials and hospital administrators alike agree that improved obstetric reporting is necessary, there is little discussion regarding the accuracy of the data that are submitted and the institutional pressures that may contribute to the production of inaccurate data. Using ethnographic research collected in Tulum, Quintana Roo, this paper explores how biomedical childbirth functions as a source of legitimization for the state while simultaneously providing the means for the presentation of an ideal subjecthood, one that situates birthing women and healthcare personnel as properly attenuated to the norms and needs of the modern Mexican state. By highlighting the point of disjuncture between women's experiences and the formal 'reality' created through hospital texts, this paper explores the place of biomedical birth as a producer of and legitimization for Mexican public health policy.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".