Critical ethnography: extending attention to bias and reinforcement of dominant power relations
Bibliographic record
Abstract
AIM: Many forms of critical theory shape nursing research. One approach that is gaining popularity is Carspecken's critical ethnography. This methodology was designed to help researchers understand power relations by merging a critical stance with a complex and dynamic qualitative strategy of inquiry. Despite the appeal of this approach, we argue Carspecken does not adequately address how researchers can minimise researcher biases and dominance when studying power relations. The purpose of this article is to critique Carspecken's implementation of critical theory from the standpoint of lack of attention to bias and reinforcement of oppression. DISCUSSION: The authors explain how greater attention to reflexivity, relationality and reciprocity could reduce opportunities for researcher bias and the potential for oppressive structures and dominant power relations to be reinforced. IMPLICATIONS FOR RESEARCH: Researchers can better address bias and reinforcement of power relations through reflexivity. Bias can be reduced by making explicit values that may privilege particular decisions and observations throughout the research process. Researchers can also reduce bias via relationality or giving participants equal power in decision making and taking action towards social justice. When researchers engage in reciprocity they encourage participants to contribute to all stages of the research process and therein equalise power relations.
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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 | 0.181 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| 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".