Politically Sensitive Encounters: Ethnography, Access, and the Benefits of “Hanging Out”
Bibliographic record
Abstract
Negotiating politically sensitive research environments requires both a careful consideration of the methods involved and a great deal of personal resolve. In drawing upon two distinct yet comparable fieldwork experiences, this paper champions the benefits of ethnographic methods in seeking to gain positionality and research legitimacy among those identified as future research participants. The authors explore and discuss their use of the ethnographic concept of “hanging out” in politically sensitive environments when seeking to negotiate access to potentially hard to reach participants living in challenging research environments. Through an illustrative examination of their experiences in researching commemorative rituals in Palestine and mental health in a Northern Irish prison, both authors reflect upon their use of “hanging out” when seeking to break down barriers and gain acceptance among their target research participants. Their involvement in a range of activities, not directly related to the overall aims of the research project, highlights a need for qualitative researchers to adopt a flexible research design, one that embraces serendipitous or chance encounters, when seeking to gain access to hard to reach research participants or when issues of researcher legitimacy are particularly pronounced, such as is the case in politically sensitive research environments.
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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 | 0.060 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.050 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| 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".