Stakeholder mapping: advancing research on sexual and reproductive health policies and income protection for cisgender and transgender female sex workers in Buenos Aires, Argentina
Bibliographic record
Abstract
Introduction: In the initial steps towards the development of an implementation project aimed to support sexual and reproductive health (SRH) policies and income protection for cisgender and transgender sex workers in the Ciudad Autónoma de Buenos Aires (CABA), we employed stakeholder mapping. This is a crucial tool in health policy and systems and research to identify, categorize, and characterize key stakeholders involved in policy planning and implementation. Methods: Prospective stakeholder mapping was conducted between February and September 2023 through a series of internal meetings and consultations with relevant community organizations to identify key stakeholders involved in SRH of female sex workers (FSWs) in CABA. The stakeholder mapping included three stages: 1. Identification and categorization of stakeholders using primary and secondary sources; 2. Analysis of stakeholder knowledge, level of agreement/interest, and level of influence/power; and 3. Characterization of stakeholder positioning. The absolute and relative frequencies of key stakeholders were estimated, and the average values of knowledge, power/influence, and interest/agreement were calculated for each category. The results were represented in a matrix identifying six types of positions (promoter, supporter, neutral, observer, high-risk blocker, low-risk blocker). Results: A total of 147 key actors were identified across sectors, including government, civil society, academia, abolitionist community organizations, health services, media and national and jurisdictional governments. Only four categories had detailed knowledge of the SRH situation and policies focused on FSWs. The stakeholders were categorized as 16% as promoters, 68% as supporters, 10% as blockers, 3% as observers, and 3% as neutral. Among promoters, national and jurisdictional governments stood out, while the supporters included the FSWs and the civil society organizations representing them, who also actively participated in the mapping process. Blockers mainly included abolitionist community organizations and security forces. Discussion: Stakeholder mapping proved to be a valuable tool for understanding the political landscape while ethically centering the voices of FSWs. The findings support the development of inclusive, context-sensitive policies and provide a replicable methodology for similar initiatives in other socio-political contexts.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".