Ethics in community research: reflections from ethnographic research with First Nations people in the US
Bibliographic record
Abstract
To share the historic implications of unethical research To discuss ethical considerations of importance to community research To share guidelines for researchers to follow in conducting research To discuss the dimensions and development of an Institutional Review Board (IRB) document Conducting ethical research may be considered one of the most important aspects of the research process. For community research, specific issues may arise that researchers must address. These are the focus of this chapter. The key methodological issues we consider in this chapter include the importance of collaboration with community members, informed consent and cultural considerations when conducting ethical research. Our experience with community research, in part, stems from our work conducting ethnographic research with the Muscogee (Creek) Nation of Oklahoma (US) and the Eastern Band of the Cherokee Nation (EBCN) of North Carolina (US) over the past 15 years. Examples from our work are included. In this chapter, the terms ‘aboriginal’, ‘indigenous’ and ‘native’ reflect the original people of a country. During the last century, various studies have illustrated the need for the creation of Institutional Review Boards (IRBs) (Berg, 2001). The torture, dismemberment and experimentation on prisoners in Nazi concentration camps (Franzblau, 1995; Howell, 1999), the Tuskegee syphilis study (Christians, 2000) and the Milgram experiment (Christians, 2000; Berg, 2001), among others, serve as instances of exploitation of participants and communities. Concerns about such studies provided the impetus for the development of guidelines to oversee research, to prohibit, or at least limit, such exploitation. In response, the US National Research Act of 1974 required all institutions conducting research to establish IRBs to guard participant safety.
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 | 0.058 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.056 | 0.064 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.015 |
| 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".