Conducting Ethical Field Research on Rape in West African Settings: Case Study of 2018 Liberian Field Survey
Bibliographic record
Abstract
BACKGROUND: Rape scholarship in West Africa is growing, but studies often utilize Westernized approaches. A 2018 study using a randomized survey design assessing rape among Liberian girls incorporated modified survey design methods to improve ethical data collection relevant to the cultural and contextual contexts. This article presents the findings of a thorough review of rape scholarship and design methods. METHODS: Based on a qualitative analysis of field notes by the research team, we present lessons learned and best practices identified in the planning, pilot-testing, and implementation phases of the 2018 Liberian study. RESULTS: This study helps inform innovative design methods striving to (1) avoid using obtrusively graphic language or labels prevalent in westernized studies, (2) authentically collaborate with African experts to adapt strategies to be culturally appropriate and contextually relevant, and (3) create respectfully transparent interactions with respondents and communities. Extensive research preparation and inclusive regional expertise inform compassionate methodological techniques, yielding improved Afro-centric participant experience, low participant attrition, and quality data use in policymaking. (4) Conclusions: This article offers innovative design methods to study rape, placing context, culture, and participants at the heart. Authentic collaboration with national-level experts is vital for conducting more reliable and ethical field research in the African region.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".