Skin Connections: Negotiating Institutional Ethics alongside Insider Identities
Bibliographic record
Abstract
FIELDWORK RAISES ETHICAL and practical challenges, all the more so in conflict or post-conflict settings. University-based ethics review boards in Canada and the United States are rarely attuned to the specific challenges and opportunities of conducting qualitative research with people living in these settings (Cronin-Furman and Lake 2018; Wood 2006). Some scholars treat ethical review as an institutional hurdle, even when the process helps prepare the researcher for ‘the field’ (Thomson 2013b). During field research, ‘the responsibility to act ethically rests ultimately on the individual researcher’ (Fujii 2012, 718. See also MacLean et a l. 2019; Wood 2006). For some field-based researchers, there is data that ‘simply cannot be accessed without an immeasurable degree of risk’ (Kovats-Bernat 2002, 210). Researchers working in volatile and violent situations face risks but, of course, they are not of the same nature (Berry et al . 2017). As such, maintaining one's ethical sensibility is not always straightforward; indeed, maintaining one's ethical commitments is a process full of uncertainty (Hutchinson 2011). For instance, Nilan (2002), after a period of emotionally fraught fieldwork, wondered, ‘whether it is [an] ethical practice to merely observe young people engaged in criminal and high-risk behaviour without warning them in any way, or notifying anyone about it. Or, indeed, whether it is ethical to eavesdrop on other people's private conversations, without letting them know you can understand what they are saying’ (381). I faced similar issues. Informed consent, the safety of my research participants, and managing expectations of benefits are made more poignant by my presence in a volatile research site, as a young African man (in his early 30s), from Benin, working in Bangui, Central African Republic (CAR). I soon realised that my research process and maintenance of ethical practice was informed not only by my class standing but also by social codes and norms of masculinity. I argue that following ethical rules must be read according to researchers’ identities. I do this showing how my skin connection and Africanity shaped my field research. To explain how I managed my ethical commitments, to myself and my participants, I draw on my six months of fieldwork in CAR in 2017.
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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 | 0.044 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.083 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".