Negotiating Positionality as a Student and Researcher in Africa: Understanding How Seniority and Race Mediate Elite Interviews in African Social Contexts
Bibliographic record
Abstract
Abstract This article takes a reflexive look at the dilemmas and challenges of accessing a predominantly male circle of political and nongovernmental elites in the Central African Republic from the perspective of a young Black African male student researcher. It focuses on questions of positionality, arguing that certain African social norms regarding seniority and hierarchy can affect data generation, specifically access and interactions within interviews. The article argues that the author's identities as a student and researcher complicated access to male and senior elite interviewees during field research, thus illustrating anew how diasporic Africans might experience the field research exercise differently even if accessing elites is generally a difficult exercise. This article contributes to understanding power differentials among interviewers, including differences among students and researchers, and the influence of race during fieldwork by African scholars. This is within an emerging literature on fieldwork that focuses on graduate students in International Relations and Comparative Politics.
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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 | 0.043 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".