Conducting Research in Authoritarian Bureaucracies: Researcher Positionality, Access, Negotiation, Cooperation, Trepidation, and Avoiding the Influence of the Gatekeepers
Bibliographic record
Abstract
During Islam Karimov’s presidency in Uzbekistan, between 1991 and 2016, the government had a complex, repressive, and paradoxical relationship with Islam. Widespread persecution, fabricated crimes, and unfair treatment of Muslims were common. Therefore, investigating the relationship between the state and Islam involves significant political risk, which has an intimidating effect on both gatekeepers and participants. Based on the field research I conducted, this paper offers insights about what to expect when conducting research in strictly controlled states like Uzbekistan. Identifying the right gatekeepers who can grant or deny access to research sites, obtaining qualifying permissions, and negotiating and collaborating with gatekeepers are important to gain access to and remain in the relevant research sites for the study. This paper contributes to the literature on conducting qualitative research in authoritarian states. The researcher positionality and their role as an insider or outsider are important parts of such research; however, they also present challenges for researchers. The discussions of reflexivity and the reflexivity of discomfort can guide researchers who face similar challenges in the field. This paper also contributes to the understanding of the importance of considering gatekeeping structures in an effort to advance qualitative research methods and research ethics.
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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 | 0.081 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.008 |
| 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".